Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

(ii) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of Bol features to multiple low-dimensional user representations by contrastive learning.

The review comment to be evaluated is:

Strengths:

--Previous work cannot model universal user representation based on lifelong sequences of user behavior since registration and challenging to make full use of highly long user behavior sequences accumulated in Internet service platforms.

-- This work proposes a universal Lifelong User Representation Model to encode user behaviors in any period with a sparse vector with super-high dimension and map these user features to user representations with almost lossless dimensionality reduction. In this way, the model could easily learn different aspects of user preferences in extremely long sequences and generalize to other downstream tasks.

Weaknesses:

-- The structure of related works is not clear enough. At the end of universal user modeling, this work focuses on the conclusion that previous work dedicated to learning universal user representations cannot leverage information from lifelong user behaviors instead of their generalization ability. The same problem also appeared at the end of lifelong user modeling.

-- In a multi-scale aggregation module, this work captures diverse patterns from user behavior by aggregating several representations generated at different time granularities. However, this method could only use information intra-corresponding representation of every time granularity and cannot explore the information inter different time granularities.

-- The explanation of the considerable gap between BoI and LURM on the two datasets is unconvincing. On the industrial dataset, the performance of BOI is better than LURM, which proved that the design of SMEN cannot bring benefits for BOI.

-- In different downstream tasks, the benefits on category preference identification tasks are tiny, even using behaviors of 5 years on this task. There is no explanation about this phenomenon, and it proves BOI cannot learn much preference information about user behaviors on different categories.

-- This work did not design experiments about multi-scale aggregation modules to prove the effectiveness of process representations of different time granularities.

-- There are some format problems in the paper, such as no period at the end of the first paragraph of the introduction.

The evidence to be evaluated is:For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016). Given a set of users \(U=\{u_{1},u_{2},...,u_{|U|}\}\), each user \(u\in U\) corresponds to a behavior sequence \(\mathbf{S}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathcal{S}|}}\big{\}}\), where \(\mathbf{x_{i}}\in\mathbf{S}\) denotes the \(i\)-th item. \(|\mathbf{U}|\) and \(|\mathbf{S}|\) denote the number of users and the length of \(u\)'s behaviors respectively. Generally, the content of an item \(\mathbf{x}\) can be expressed as \(\{w_{1},w_{2},...,w_{|\mathbf{x}|}\}\), where \(w_{i}\) denotes a word from a vocabulary \(V\), and \(|\mathbf{x}|\) denotes the number of words in the content of \(\mathbf{x}\). Firstly, an encoder with average operation is used to generate item embedding \(c\): \[\mathbf{e_{\mathbf{x}}}=encoder(w_{1},w_{2},...,w_{|\mathbf{x}|})=proj(\frac{1}{|\mathbf{x}|} \sum_{i=1}^{|\mathbf{x}|}\mathbf{W_{i}}), \tag{1}\] where \(\mathbf{W_{i}}\in\mathbb{R}^{d}\) is the embedding of word \(w_{i}\) and will be learned during training, \(proj(\cdot)\) includes two residual blocks, and a \(L_{2}\) normalization layer. To construct the contrastive learning task, we sample positive pairs from behavior sequences of users randomly. Specifically, two items \((\mathbf{x_{i}},\mathbf{y_{i}})\) are similar, i.e. a positive pair, if they are drawn from the same user behavior sequence and the time interval between the occurrence of these two items is less than \(\beta\), where \(\beta\) is the window size controlling the interval of the two user behaviors. Without loss of generality, the sampled mini-batch with batch size \(n\) can be denoted as \(\mathbf{\Delta}=\{\mathbf{x^{1}},\mathbf{y^{1}},\mathbf{x^{2}},\mathbf{y^{2}},...,\mathbf{x^{n}},\bm {y^{n}}\}\), where \((\mathbf{x^{i}},\mathbf{y^{i}})\) construct a positive pair drawn from the behavior sequence \(\mathbf{S^{i}_{t}}\) of the \(i\)-th user in batch. Then, the contrastive prediction task is defined to identify \(\mathbf{y^{i}}\) in \(\mathbf{\Delta}\setminus\{\mathbf{x^{i}}\}\) for a given \(\mathbf{x^{i}}\), and all other items in \(\mathbf{\Delta}\setminus\{\mathbf{x^{i}},\mathbf{y^{i}}\}\) are negatives. The loss for the positive pair \((\mathbf{x^{i}},\mathbf{y^{i}})\) is written as \[l(\mathbf{x^{i}},\mathbf{y^{i}})=-\log\frac{e^{g(\mathbf{x^{i}},\mathbf{y^{i}})/\tau}}{\sum_{ \nu\in\mathbf{\Delta},\nu\neq\mathbf{x^{i}}}e^{g(\mathbf{x^{i}},\mathbf{\nu})/\tau}}, \tag{2}\] where \(g(\mathbf{x},\mathbf{y})=\frac{\mathbf{e^{T}_{x}}\mathbf{e_{y}}}{\|\mathbf{e_{x}}\|\mathbf{e_{y}}\|}= \mathbf{e^{T}_{x}}\mathbf{e_{y}}\) denotes the cosine similarity between the embedding \(\mathbf{e_{x}}\) and the embedding \(\mathbf{e_{y}}\), and \(\tau\) is the temperature parameter.

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Finally, a group of different aggregated representations \(\mathbf{R}=\{\mathbf{r^{1}},\mathbf{r^{2}},...,\mathbf{r^{M}}\}\) can be obtained (\(\mathbf{M}\) indicates the total number of anchors). In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

Under a time division \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\), we have obtained \(M\) representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) through the BoI, multi-anchor module and time aggregation module as introduced above.

The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation. As mentioned in 3.1.1, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. \(h(\cdot)\), as used in SimCLR Chen et al. (2020). The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\]

Two behavior sequences drawn from the same user are treated as a positive pair, and behavior sequences from other users are negatives. All sequences are continuous sub-sequences randomly sampled from the whole life time \(\mathbf{T}\). Finally, the contrastive loss is defined the same as equation (3).

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 1: Illustration of our Lifelong User Representation Model (LURM) for user understanding. LURM consists of two sub-model: (a) Bag of Interests (BoI) is used to aggregate user behavior data at ‘interest’ granularity, and is composed of an item embedding module and a large scale clustering module. We apply the BoI model on lifelong behavior data to generate multi-scale BoI feature sequences, (b) Self-supervised Multi-anchor Encoder Network (SMEN) is used to learn compressed user representations from BoI features sequences, and is composed of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
I'll evaluate the claim: "Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of Bol features to multiple low-dimensional user representations by contrastive learning."

The evidence strongly supports this claim through detailed mathematical formulations and architectural descriptions. The evidence shows that SMEN:
1. Uses multi-anchor modules to convert BoI features to multiple low-dimensional representations
2. Employs contrastive learning through positive/negative pairs
3. Includes time aggregation and multi-scale aggregation modules
4. Maps BoI feature sequences to compressed user representations

The claim is precise and accurately reflects the technical functionality without exaggeration. The statement aligns well with both the textual descriptions and architectural diagrams provided.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

(ii) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of Bol features to multiple low-dimensional user representations by contrastive learning.

The review comment to be evaluated is:

Strengths:

1. This paper is easy to understand.
2. The problem studied in this paper is important.
3. The proposed multi-scale aggregation method is interesting.

Weaknesses:

1. The contribution of this paper can be somewhat incremental. First, the multi-anchor attention network is also called "multi-aspect attention", which has been widely used in many scenarios [1,2,3]. A very similar contrastive model pertaining approach is also studied in [4]. Thus, the contributions of this paper are somewhat incremental.
2. Discussions and comparisons on universal user modeling methods are insufficient. The authors only include PTUM as the baseline but do not discuss it in Section 2.1. There are also many other methods like [4,5]. They are also not cited nor compared.
3. The design of the item embedding method is problematic. Different from texts that there are rich relations between adjacent words, even adjacent behaviors can be rather random. Thus, it may not be suitable to learn item embeddings by regarding items as words and user behavior sequences as sentences. There are many methods for item embedding learning such as PinSage [6].
4. Compared baselines are also insufficient. As mentioned in the second point, there are many user model pretraining methods that are not compared. In addition, the compared basic user modeling techniques are also rather weak (e.g., CNN, HAN, Doc2Vec and TF-IDF).
5. Some details are unclear. PTUM and PeterRec require fine-tuning the user model in downstream tasks. However, in this work it is not clear whether the pretrained user model is finetuned.
6. The selection of some hyperparameters seems to be arbitrary, e.g., the number of anchors and the window size $\beta$. The authors do not provide experiments to show the influence of several key hyperparameters.
7. The generality of the proposed method is not verified. It is not clear whether the proposed method can be generalized to other types of user models.

Minor:

There are some typos and grammatical issues. For example, "committed to learn" and "Comparisonn".

[1] Event detection using hierarchical multi-aspect attention. WWW 2019.

[2] ARP: Aspect-aware neural review rating prediction. CIKM 2019.

[3] Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23.6 (2020): 3125-3151.

[4] Contrastive Pre-training for Sequential Recommendation. arXiv e-prints (2020): arXiv-2010.

[5] Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. SIGIR. 2020.

[6] Graph convolutional neural networks for web-scale recommender systems. KDD 2018.

The evidence to be evaluated is:For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016). Given a set of users \(U=\{u_{1},u_{2},...,u_{|U|}\}\), each user \(u\in U\) corresponds to a behavior sequence \(\mathbf{S}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathcal{S}|}}\big{\}}\), where \(\mathbf{x_{i}}\in\mathbf{S}\) denotes the \(i\)-th item. \(|\mathbf{U}|\) and \(|\mathbf{S}|\) denote the number of users and the length of \(u\)'s behaviors respectively. Generally, the content of an item \(\mathbf{x}\) can be expressed as \(\{w_{1},w_{2},...,w_{|\mathbf{x}|}\}\), where \(w_{i}\) denotes a word from a vocabulary \(V\), and \(|\mathbf{x}|\) denotes the number of words in the content of \(\mathbf{x}\). Firstly, an encoder with average operation is used to generate item embedding \(c\): \[\mathbf{e_{\mathbf{x}}}=encoder(w_{1},w_{2},...,w_{|\mathbf{x}|})=proj(\frac{1}{|\mathbf{x}|} \sum_{i=1}^{|\mathbf{x}|}\mathbf{W_{i}}), \tag{1}\] where \(\mathbf{W_{i}}\in\mathbb{R}^{d}\) is the embedding of word \(w_{i}\) and will be learned during training, \(proj(\cdot)\) includes two residual blocks, and a \(L_{2}\) normalization layer. To construct the contrastive learning task, we sample positive pairs from behavior sequences of users randomly. Specifically, two items \((\mathbf{x_{i}},\mathbf{y_{i}})\) are similar, i.e. a positive pair, if they are drawn from the same user behavior sequence and the time interval between the occurrence of these two items is less than \(\beta\), where \(\beta\) is the window size controlling the interval of the two user behaviors. Without loss of generality, the sampled mini-batch with batch size \(n\) can be denoted as \(\mathbf{\Delta}=\{\mathbf{x^{1}},\mathbf{y^{1}},\mathbf{x^{2}},\mathbf{y^{2}},...,\mathbf{x^{n}},\bm {y^{n}}\}\), where \((\mathbf{x^{i}},\mathbf{y^{i}})\) construct a positive pair drawn from the behavior sequence \(\mathbf{S^{i}_{t}}\) of the \(i\)-th user in batch. Then, the contrastive prediction task is defined to identify \(\mathbf{y^{i}}\) in \(\mathbf{\Delta}\setminus\{\mathbf{x^{i}}\}\) for a given \(\mathbf{x^{i}}\), and all other items in \(\mathbf{\Delta}\setminus\{\mathbf{x^{i}},\mathbf{y^{i}}\}\) are negatives. The loss for the positive pair \((\mathbf{x^{i}},\mathbf{y^{i}})\) is written as \[l(\mathbf{x^{i}},\mathbf{y^{i}})=-\log\frac{e^{g(\mathbf{x^{i}},\mathbf{y^{i}})/\tau}}{\sum_{ \nu\in\mathbf{\Delta},\nu\neq\mathbf{x^{i}}}e^{g(\mathbf{x^{i}},\mathbf{\nu})/\tau}}, \tag{2}\] where \(g(\mathbf{x},\mathbf{y})=\frac{\mathbf{e^{T}_{x}}\mathbf{e_{y}}}{\|\mathbf{e_{x}}\|\mathbf{e_{y}}\|}= \mathbf{e^{T}_{x}}\mathbf{e_{y}}\) denotes the cosine similarity between the embedding \(\mathbf{e_{x}}\) and the embedding \(\mathbf{e_{y}}\), and \(\tau\) is the temperature parameter.

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Finally, a group of different aggregated representations \(\mathbf{R}=\{\mathbf{r^{1}},\mathbf{r^{2}},...,\mathbf{r^{M}}\}\) can be obtained (\(\mathbf{M}\) indicates the total number of anchors). In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

Under a time division \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\), we have obtained \(M\) representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) through the BoI, multi-anchor module and time aggregation module as introduced above.

The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation. As mentioned in 3.1.1, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. \(h(\cdot)\), as used in SimCLR Chen et al. (2020). The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\]

Two behavior sequences drawn from the same user are treated as a positive pair, and behavior sequences from other users are negatives. All sequences are continuous sub-sequences randomly sampled from the whole life time \(\mathbf{T}\). Finally, the contrastive loss is defined the same as equation (3).

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 1: Illustration of our Lifelong User Representation Model (LURM) for user understanding. LURM consists of two sub-model: (a) Bag of Interests (BoI) is used to aggregate user behavior data at ‘interest’ granularity, and is composed of an item embedding module and a large scale clustering module. We apply the BoI model on lifelong behavior data to generate multi-scale BoI feature sequences, (b) Self-supervised Multi-anchor Encoder Network (SMEN) is used to learn compressed user representations from BoI features sequences, and is composed of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on the evidence provided, I'll evaluate the claim "Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of Bol features to multiple low-dimensional user representations by contrastive learning."

The evidence strongly supports this claim. The paper provides detailed mathematical formulations and architectural descriptions showing how SMEN:
- Uses multi-anchor modules to map BoI features to multiple representations
- Employs contrastive learning (equations 2 and similar to equation 3)
- Creates low-dimensional representations through time aggregation and multi-scale aggregation modules

The claim is precise and accurately reflects the technical details without exaggeration. The review comments, while noting some incremental aspects and similarity to existing methods, don't dispute the fundamental technical claims about SMEN's functionality.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

(ii) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of Bol features to multiple low-dimensional user representations by contrastive learning.

The review comment to be evaluated is:

[Advantages]
- Modeling users and user behaviors are essential, especially for web-based applications. The problem dealt with in this paper will be a promising application of representation learning based on neural networks.
- The structure of the proposed method is reasonable, especially for e-commerce applications.

[Drawbacks]
- I could not understand the novelty of the proposed method. The bag-of-features representation seems to be standard in several research fields such as computer vision, natural language processing, and data mining. The "multi-anchor module" seems to be almost the same as the multi-head attention module that is famous in the transformer. The time aggregation module does not contain a special trick. According to the introduction of this paper, the novelty of the proposed method is the ability for modeling life-long behaviors. However, I could not find any special techniques for handling life-long sequences. Please clearly state the technical novelty of the proposed method.
- The proposed method for modeling user behaviors dismisses both microscopic and macroscopic time-series characteristics, which would be significant for tracing user interest. More specifically, bag-of-interest representations remove microscopic characteristics, and the time aggregation module erases macroscopic features. If the authors believe that I have some misunderstanding, please justify it.
- When obtaining stable user representations, the knowledge of life-long learning will be required, since (1) users might have several completely different interests in their minds, (2) each interest lasts only a short term, especially in e-commerce applications, and (3) interests containing old behavior sequences might come back suddenly. This implies that almost all the information containing behavior sequences should be kept in user representations and this problem is exactly in the context of life-long learning. Please perform bibliographic surveys for life-long learning, describe the critical problem for the previous work, and present the novelty and advantage(s)  of the proposed method against the previous work.
- The downstream tasks in the experimental evaluations seem to be too easy and not suitable for evaluating the ability to handle long behavior sequences.

The evidence to be evaluated is:For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016). Given a set of users \(U=\{u_{1},u_{2},...,u_{|U|}\}\), each user \(u\in U\) corresponds to a behavior sequence \(\mathbf{S}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathcal{S}|}}\big{\}}\), where \(\mathbf{x_{i}}\in\mathbf{S}\) denotes the \(i\)-th item. \(|\mathbf{U}|\) and \(|\mathbf{S}|\) denote the number of users and the length of \(u\)'s behaviors respectively. Generally, the content of an item \(\mathbf{x}\) can be expressed as \(\{w_{1},w_{2},...,w_{|\mathbf{x}|}\}\), where \(w_{i}\) denotes a word from a vocabulary \(V\), and \(|\mathbf{x}|\) denotes the number of words in the content of \(\mathbf{x}\). Firstly, an encoder with average operation is used to generate item embedding \(c\): \[\mathbf{e_{\mathbf{x}}}=encoder(w_{1},w_{2},...,w_{|\mathbf{x}|})=proj(\frac{1}{|\mathbf{x}|} \sum_{i=1}^{|\mathbf{x}|}\mathbf{W_{i}}), \tag{1}\] where \(\mathbf{W_{i}}\in\mathbb{R}^{d}\) is the embedding of word \(w_{i}\) and will be learned during training, \(proj(\cdot)\) includes two residual blocks, and a \(L_{2}\) normalization layer. To construct the contrastive learning task, we sample positive pairs from behavior sequences of users randomly. Specifically, two items \((\mathbf{x_{i}},\mathbf{y_{i}})\) are similar, i.e. a positive pair, if they are drawn from the same user behavior sequence and the time interval between the occurrence of these two items is less than \(\beta\), where \(\beta\) is the window size controlling the interval of the two user behaviors. Without loss of generality, the sampled mini-batch with batch size \(n\) can be denoted as \(\mathbf{\Delta}=\{\mathbf{x^{1}},\mathbf{y^{1}},\mathbf{x^{2}},\mathbf{y^{2}},...,\mathbf{x^{n}},\bm {y^{n}}\}\), where \((\mathbf{x^{i}},\mathbf{y^{i}})\) construct a positive pair drawn from the behavior sequence \(\mathbf{S^{i}_{t}}\) of the \(i\)-th user in batch. Then, the contrastive prediction task is defined to identify \(\mathbf{y^{i}}\) in \(\mathbf{\Delta}\setminus\{\mathbf{x^{i}}\}\) for a given \(\mathbf{x^{i}}\), and all other items in \(\mathbf{\Delta}\setminus\{\mathbf{x^{i}},\mathbf{y^{i}}\}\) are negatives. The loss for the positive pair \((\mathbf{x^{i}},\mathbf{y^{i}})\) is written as \[l(\mathbf{x^{i}},\mathbf{y^{i}})=-\log\frac{e^{g(\mathbf{x^{i}},\mathbf{y^{i}})/\tau}}{\sum_{ \nu\in\mathbf{\Delta},\nu\neq\mathbf{x^{i}}}e^{g(\mathbf{x^{i}},\mathbf{\nu})/\tau}}, \tag{2}\] where \(g(\mathbf{x},\mathbf{y})=\frac{\mathbf{e^{T}_{x}}\mathbf{e_{y}}}{\|\mathbf{e_{x}}\|\mathbf{e_{y}}\|}= \mathbf{e^{T}_{x}}\mathbf{e_{y}}\) denotes the cosine similarity between the embedding \(\mathbf{e_{x}}\) and the embedding \(\mathbf{e_{y}}\), and \(\tau\) is the temperature parameter.

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Finally, a group of different aggregated representations \(\mathbf{R}=\{\mathbf{r^{1}},\mathbf{r^{2}},...,\mathbf{r^{M}}\}\) can be obtained (\(\mathbf{M}\) indicates the total number of anchors). In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

Under a time division \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\), we have obtained \(M\) representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) through the BoI, multi-anchor module and time aggregation module as introduced above.

The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation. As mentioned in 3.1.1, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. \(h(\cdot)\), as used in SimCLR Chen et al. (2020). The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\]

Two behavior sequences drawn from the same user are treated as a positive pair, and behavior sequences from other users are negatives. All sequences are continuous sub-sequences randomly sampled from the whole life time \(\mathbf{T}\). Finally, the contrastive loss is defined the same as equation (3).

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 1: Illustration of our Lifelong User Representation Model (LURM) for user understanding. LURM consists of two sub-model: (a) Bag of Interests (BoI) is used to aggregate user behavior data at ‘interest’ granularity, and is composed of an item embedding module and a large scale clustering module. We apply the BoI model on lifelong behavior data to generate multi-scale BoI feature sequences, (b) Self-supervised Multi-anchor Encoder Network (SMEN) is used to learn compressed user representations from BoI features sequences, and is composed of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence:

The claim "(ii) Self-supervised Multi-anchor Encoder Network (SMEN) maps sequences of Bol features to multiple low-dimensional user representations by contrastive learning" is well-stated and accurately reflects the evidence without exaggeration.

The evidence thoroughly demonstrates that SMEN:
- Uses a multi-anchor module to transform BoI features into multiple low-dimensional representations
- Employs contrastive learning through positive/negative pair sampling
- Maps sequences through time aggregation and multi-scale aggregation modules
- The technical details and mathematical formulations support each component mentioned in the claim

While the reviewer notes concerns about novelty and time-series characteristics, these don't affect the accuracy of this specific descriptive claim about SMEN's core functionality.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences.

The evidence to be evaluated is:In this work, we are committed to learning universal user representation through truly lifelong user behavior sequences with arbitrary length. For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Every 'interest' is a cluster of similar items and represents a certain kind of preference.

Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together. Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016).

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

The data of user behavior, which is highly unstructured and complex, implies different preferences of the user. To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Due to the different anchor vectors, different attention weights can be generated for each 'interest'.

In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

To address this trade-off, multi-scale aggregation module is designed. The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation.

The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

In order to retain details as many as possible, the size of the 'interest' vocabulary was set to \(10^{5}\), and the number of anchors \(M\) was set to 10 in our experiments. So the final user embedding dimension was 1280.

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

Taking BoI as a benchmark, it can be seen that LURM achieves almost lossless dimensionality reduction compared to LURM(1anchor-128) and LURM(1anchor-1280), which verifies the effectiveness of the multi-anchor module.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find this claim to be moderately overstated. While the evidence shows SMEN achieves strong dimensionality reduction through its multi-anchor module, the term "almost lossless" is too strong given the available data. The evidence shows improvements and effectiveness but doesn't conclusively prove lossless compression. The paper indicates "similar results compared with BoI" and "ability to compress representation losslessly" but lacks rigorous quantitative proof of truly lossless reduction. The claim would be better stated by acknowledging it achieves effective or efficient dimensionality reduction rather than claiming "almost lossless" performance.

<score>3</score>

This middle score reflects that while SMEN does demonstrate strong performance through its novel multi-anchor approach, the claim somewhat overstates the conclusiveness of the evidence regarding "almost lossless" reduction. The evidence supports SMEN's effectiveness but not to the degree of being definitively "almost lossless."

Justification:
1. The paper shows SMEN achieves good compression but uses comparative terms like "similar results" rather than proving lossless performance
2. Limited quantitative evidence is provided to support the specific claim of "almost lossless"
3. While the multi-anchor module is novel and effective, claiming "almost lossless" reduction requires stronger empirical validation
4. The evidence better supports a more measured claim about effective dimensionality reduction rather than "almost lossless" performance


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences.

The review comment to be evaluated is:

Strengths:

--Previous work cannot model universal user representation based on lifelong sequences of user behavior since registration and challenging to make full use of highly long user behavior sequences accumulated in Internet service platforms.

-- This work proposes a universal Lifelong User Representation Model to encode user behaviors in any period with a sparse vector with super-high dimension and map these user features to user representations with almost lossless dimensionality reduction. In this way, the model could easily learn different aspects of user preferences in extremely long sequences and generalize to other downstream tasks.

Weaknesses:

-- The structure of related works is not clear enough. At the end of universal user modeling, this work focuses on the conclusion that previous work dedicated to learning universal user representations cannot leverage information from lifelong user behaviors instead of their generalization ability. The same problem also appeared at the end of lifelong user modeling.

-- In a multi-scale aggregation module, this work captures diverse patterns from user behavior by aggregating several representations generated at different time granularities. However, this method could only use information intra-corresponding representation of every time granularity and cannot explore the information inter different time granularities.

-- The explanation of the considerable gap between BoI and LURM on the two datasets is unconvincing. On the industrial dataset, the performance of BOI is better than LURM, which proved that the design of SMEN cannot bring benefits for BOI.

-- In different downstream tasks, the benefits on category preference identification tasks are tiny, even using behaviors of 5 years on this task. There is no explanation about this phenomenon, and it proves BOI cannot learn much preference information about user behaviors on different categories.

-- This work did not design experiments about multi-scale aggregation modules to prove the effectiveness of process representations of different time granularities.

-- There are some format problems in the paper, such as no period at the end of the first paragraph of the introduction.

The evidence to be evaluated is:In this work, we are committed to learning universal user representation through truly lifelong user behavior sequences with arbitrary length. For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Every 'interest' is a cluster of similar items and represents a certain kind of preference.

Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together. Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016).

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

The data of user behavior, which is highly unstructured and complex, implies different preferences of the user. To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Due to the different anchor vectors, different attention weights can be generated for each 'interest'.

In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

To address this trade-off, multi-scale aggregation module is designed. The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation.

The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

In order to retain details as many as possible, the size of the 'interest' vocabulary was set to \(10^{5}\), and the number of anchors \(M\) was set to 10 in our experiments. So the final user embedding dimension was 1280.

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

Taking BoI as a benchmark, it can be seen that LURM achieves almost lossless dimensionality reduction compared to LURM(1anchor-128) and LURM(1anchor-1280), which verifies the effectiveness of the multi-anchor module.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences" appears to be somewhat overstated. While the evidence shows SMEN performs well in dimensionality reduction through its multi-anchor module, there are important caveats:

1. The reviewer points out lack of experimental validation for multi-scale aggregation effectiveness
2. The industrial dataset shows similar (not superior) results to BoI, suggesting limitations
3. The evidence shows mixed results across different tasks, with only modest benefits in some cases
4. The claim of "almost lossless" is strong given the limited experimental validation presented

<score>3</score>

Justification:
1. While the model shows promise, the claim uses definitive language ("achieves almost lossless") without fully acknowledging limitations or providing comprehensive experimental validation.
2. The reviewer notes several gaps in experimental validation, particularly regarding multi-scale aggregation.
3. The evidence from the industrial dataset shows LURM achieving "similar results compared with BoI" rather than definitively superior performance.
4. The benefits are inconsistent across different tasks, with "tiny" improvements in some areas according to the reviewer.
5. The multi-anchor mechanism is indeed novel and shows potential, but claiming "almost lossless" reduction may overstate the current evidence.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences.

The review comment to be evaluated is:

Strengths:

1. This paper is easy to understand.
2. The problem studied in this paper is important.
3. The proposed multi-scale aggregation method is interesting.

Weaknesses:

1. The contribution of this paper can be somewhat incremental. First, the multi-anchor attention network is also called "multi-aspect attention", which has been widely used in many scenarios [1,2,3]. A very similar contrastive model pertaining approach is also studied in [4]. Thus, the contributions of this paper are somewhat incremental.
2. Discussions and comparisons on universal user modeling methods are insufficient. The authors only include PTUM as the baseline but do not discuss it in Section 2.1. There are also many other methods like [4,5]. They are also not cited nor compared.
3. The design of the item embedding method is problematic. Different from texts that there are rich relations between adjacent words, even adjacent behaviors can be rather random. Thus, it may not be suitable to learn item embeddings by regarding items as words and user behavior sequences as sentences. There are many methods for item embedding learning such as PinSage [6].
4. Compared baselines are also insufficient. As mentioned in the second point, there are many user model pretraining methods that are not compared. In addition, the compared basic user modeling techniques are also rather weak (e.g., CNN, HAN, Doc2Vec and TF-IDF).
5. Some details are unclear. PTUM and PeterRec require fine-tuning the user model in downstream tasks. However, in this work it is not clear whether the pretrained user model is finetuned.
6. The selection of some hyperparameters seems to be arbitrary, e.g., the number of anchors and the window size $\beta$. The authors do not provide experiments to show the influence of several key hyperparameters.
7. The generality of the proposed method is not verified. It is not clear whether the proposed method can be generalized to other types of user models.

Minor:

There are some typos and grammatical issues. For example, "committed to learn" and "Comparisonn".

[1] Event detection using hierarchical multi-aspect attention. WWW 2019.

[2] ARP: Aspect-aware neural review rating prediction. CIKM 2019.

[3] Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23.6 (2020): 3125-3151.

[4] Contrastive Pre-training for Sequential Recommendation. arXiv e-prints (2020): arXiv-2010.

[5] Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. SIGIR. 2020.

[6] Graph convolutional neural networks for web-scale recommender systems. KDD 2018.

The evidence to be evaluated is:In this work, we are committed to learning universal user representation through truly lifelong user behavior sequences with arbitrary length. For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Every 'interest' is a cluster of similar items and represents a certain kind of preference.

Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together. Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016).

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

The data of user behavior, which is highly unstructured and complex, implies different preferences of the user. To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Due to the different anchor vectors, different attention weights can be generated for each 'interest'.

In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

To address this trade-off, multi-scale aggregation module is designed. The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation.

The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

In order to retain details as many as possible, the size of the 'interest' vocabulary was set to \(10^{5}\), and the number of anchors \(M\) was set to 10 in our experiments. So the final user embedding dimension was 1280.

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

Taking BoI as a benchmark, it can be seen that LURM achieves almost lossless dimensionality reduction compared to LURM(1anchor-128) and LURM(1anchor-1280), which verifies the effectiveness of the multi-anchor module.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on the evidence and review comments, I find this claim requires careful evaluation:

The claim that "SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences" appears somewhat overstated. While the evidence does show SMEN's effectiveness in dimensionality reduction through its multi-anchor architecture, several key concerns emerge:

1. The "novel" aspect is questioned in the review, noting similar multi-aspect attention approaches exist in prior work
2. The evidence shows positive results but "almost lossless" is a strong claim that needs more robust validation
3. Limited baseline comparisons and hyperparameter justification weaken the strength of the claim
4. The generalizability of the method isn't fully verified

<score>4</score>

My detailed justification:

1. Strength of "almost lossless" claim:
- The evidence states "Taking BoI as a benchmark, it can be seen that LURM achieves almost lossless dimensionality reduction" but provides limited comparative metrics
- The review points out insufficient comparisons with other baseline methods

2. Novelty concerns:
- The review cites multiple prior works [1,2,3] using similar multi-aspect attention approaches
- The contribution is characterized as "somewhat incremental" by reviewers

3. Methodological limitations:
- The review notes arbitrary hyperparameter selection (number of anchors, window size)
- Limited verification of generalizability across different user models
- Insufficient experimental details about fine-tuning

While the method shows promise, the claim would be better stated with more measured language acknowledging limitations and prior work in the field.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences.

The review comment to be evaluated is:

[Advantages]
- Modeling users and user behaviors are essential, especially for web-based applications. The problem dealt with in this paper will be a promising application of representation learning based on neural networks.
- The structure of the proposed method is reasonable, especially for e-commerce applications.

[Drawbacks]
- I could not understand the novelty of the proposed method. The bag-of-features representation seems to be standard in several research fields such as computer vision, natural language processing, and data mining. The "multi-anchor module" seems to be almost the same as the multi-head attention module that is famous in the transformer. The time aggregation module does not contain a special trick. According to the introduction of this paper, the novelty of the proposed method is the ability for modeling life-long behaviors. However, I could not find any special techniques for handling life-long sequences. Please clearly state the technical novelty of the proposed method.
- The proposed method for modeling user behaviors dismisses both microscopic and macroscopic time-series characteristics, which would be significant for tracing user interest. More specifically, bag-of-interest representations remove microscopic characteristics, and the time aggregation module erases macroscopic features. If the authors believe that I have some misunderstanding, please justify it.
- When obtaining stable user representations, the knowledge of life-long learning will be required, since (1) users might have several completely different interests in their minds, (2) each interest lasts only a short term, especially in e-commerce applications, and (3) interests containing old behavior sequences might come back suddenly. This implies that almost all the information containing behavior sequences should be kept in user representations and this problem is exactly in the context of life-long learning. Please perform bibliographic surveys for life-long learning, describe the critical problem for the previous work, and present the novelty and advantage(s)  of the proposed method against the previous work.
- The downstream tasks in the experimental evaluations seem to be too easy and not suitable for evaluating the ability to handle long behavior sequences.

The evidence to be evaluated is:In this work, we are committed to learning universal user representation through truly lifelong user behavior sequences with arbitrary length. For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN).

Every 'interest' is a cluster of similar items and represents a certain kind of preference.

Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together. Recently, in natural language and image processing, dis criminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results. Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016).

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

The data of user behavior, which is highly unstructured and complex, implies different preferences of the user. To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are \(M\) anchors, and each of them indicates a certain preference of users. Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2. Each representation is computed as \[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix. And \(\alpha_{ij}\) is the attention weight between the \(i\)-th anchor and the \(j\)-th 'interest', which measures the portion assigned to the \(i\)-th preference from the \(j\)-th behavior 'interest'. The weight \(\alpha_{ij}\) is defined as \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

Due to the different anchor vectors, different attention weights can be generated for each 'interest'.

In this way, we can learn different aspects of user preferences. Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module. Through the multi-anchor module, a sequence of representation groups \(\mathbf{\mathcal{R}}=\{\mathbf{R_{t_{1}}},\mathbf{R_{t_{2}}},...,\mathbf{R_{t_{N}}}\}\) for each user is obtained, where \(\mathbf{R_{t_{i}}}=\{\mathbf{r_{t_{i}}^{1}},\mathbf{r_{t_{i}}^{2}},...,\mathbf{r_{t_{i}}^{M}}\}\) is a representation group generated by multi-anchor module corresponding to the BoI feature \(\mathbf{b_{t_{i}}}\) in time period \(\mathbf{t_{i}}\). In the time aggregation module, each sequence \(\mathbf{R^{i}}=\{\mathbf{r_{t_{i}}^{i}},\mathbf{r_{t_{i}}^{i}},...,\mathbf{r_{t_{N}}^{i}}\}\) is aggregated separately to yield a new representation \(\mathbf{\tilde{r}^{i}}\in\mathbb{R}^{H}\). Thus the variable-size representation sequence \(\mathbf{\mathcal{R}}\) can be transformed into \(M\) fixed-size representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

To address this trade-off, multi-scale aggregation module is designed. The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained. Then, two groups of representations \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\) and \(\mathbf{\tilde{R^{\prime}}}=\big{\{}\mathbf{\tilde{r^{\prime}}^{1}},\mathbf{\tilde{r^{ \prime}}^{2}},...,\mathbf{\tilde{r^{\prime}}^{M}}\big{\}}\) are obtained after the multi-anchor module and time aggregation module with parameter sharing.

The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation.

The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

In order to retain details as many as possible, the size of the 'interest' vocabulary was set to \(10^{5}\), and the number of anchors \(M\) was set to 10 in our experiments. So the final user embedding dimension was 1280.

Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

Taking BoI as a benchmark, it can be seen that LURM achieves almost lossless dimensionality reduction compared to LURM(1anchor-128) and LURM(1anchor-1280), which verifies the effectiveness of the multi-anchor module.

It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly.

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation.

Input: Figure 2: Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each ‘interest’ to different anchors. The • denotes the dot product operation, ⊗ denotes the scalar multiply operation, and ⊕ denotes vector sum operation.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and reviewer comments, I find the claim "SMEN achieves almost lossless dimensionality reduction, benefiting from a novel multi-anchor module which can learn different aspects of user preferences" to be somewhat overstated.

While the evidence shows SMEN performs well in dimensionality reduction, several key concerns emerge:
1. The reviewer correctly points out that the multi-anchor module is very similar to existing multi-head attention mechanisms, questioning its novelty
2. The "almost lossless" claim is only supported by comparative performance with BoI benchmark, without comprehensive theoretical or empirical validation
3. The evidence presents limited experimental validation of the lossless nature of the reduction
4. The reviewer notes the evaluation tasks may be too simple to fully validate the claims

This suggests moderate overstatement of both the novelty and performance claims.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Experiments on several benchmark datasets show that our approach outperforms state-of-the-art unsupervised representation methods in downstream tasks.

The evidence to be evaluated is:We compared the proposed LURM model against a rich set of user representation learning methods, including TF-IDF, Doc2Vec, TextCNN, HAN, PTUM and SUMN.

We also compared LURM with two methods named TextCNN and HAN which learn task-specific user representations and classifiers in a supervised manner. Finally, we compared LURM with two newly self-supervised methods for user modeling mentioned before, PTUM and SUMN.

Table 2 shows the comparison of category preference identification on the public Amazon dataset.

It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively. Even compared with supervised methods, LURM outperforms TextCNN and HAN about 8.26%/3.47% and 3.82%/2.04% respectively.

Table 3 shows the comparison of two user profiling prediction tasks and three category preference identification tasks on the industrial dataset.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks. When using longer input, we can obtain more improvements, e.g., about 15.55%/17.55% average improvement on user profiling prediction tasks, and 4.19%/2.91% average improvement on category preference identification tasks compared with SUMN. From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods.

With the ability to model lifelong user behaviors, our method shows promising results on different downstream tasks and datasets. \begin{table}
\begin{tabular}{c c c c c} \hline Method & Books of Literature & Games of Sports & Outdoor-hunting & Musical Instrument \\ \hline TextCNN & 73.847/2.83 & 61.00/69.92 & 66.06/72.76 & 68.28/71.81 \\ HAN & 79.12/77.49 & 66.62/70.01 & 71.19/73.42 & 70.01/72.12 \\ \hline TF-IDF & 78.62/77.21 & 65.69/68.69 & 71.90/72.19 & 69.16/68.62 \\ Doc2Vec & 71.21/68.26 & 66.75/66.85 & 71.58/66.36 & 70.29/67.37 \\ \hline PTUM & 78.66/77.91 & 66.06/68.97 & 70.34/73.01 & 69.50/72.92 \\ SUMN & 79.57/77.73 & 66.83/70.63 & 72.27/73.44 & 70.61/73.43 \\ \hline BoI & 76.29/75.75 & 59.34/67.58 & 67.80/71.28 & 66.36/69.88 \\ LURM & **82.55/79.13** & **68.02/72.43** & **77.59/75.19** & **74.04/74.43** \\ \hline \end{tabular}
\end{table}
Table 2: Comparison in terms of AUC(%)/ACC(%) on the Amazon dataset. Several category preference identification tasks are evaluated. The best score is bold.

 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline Method & Age & Baby Age & Clothing & Shoe & Coffee \\ \hline TextCNN & 86.40/61.02 & 72.19/66.01 & 76.64/76.13 & 84.33/80.24 & 80.49/79.46 \\ TF-IDF & 87.73/61.75 & 73.08/67.26 & 77.61/78.01 & 85.04/81.15 & 81.75/80.78 \\ SUMN & 85.35/60.61 & 74.20/67.63 & 73.86/74.16 & 83.47/79.63 & 79.89/79.42 \\ \hline BoI(short) & 89.57/63.99 & 82.23/70.22 & 78.93/78.85 & 85.53/81.68 & 82.61/81.41 \\ LURM(short) & 88.48/61.99 & 82.71/69.29 & 78.37/78.57 & 85.15/81.18 & 81.94/80.78 \\ BoI & **96.48**/**80.19** & 93.29/83.57 & **80.83**/**79.33** & **86.69**/**82.16** & **83.78**/**81.74** \\ LURM & 96.09/78.43 & **94.59**/**84.91** & 80.37/79.10 & 86.30/81.65 & 83.11/81.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison in terms of AUC/ACC on Industrial dataset. Two profiling prediction tasks and three category preference identification tasks are evaluated. The best score is bold.

Input: Table 2: Comparison in terms of AUC(%)/ACC(%) on the Amazon dataset. Several category preference identification tasks are evaluated. The best score is bold.

Input: Table 3: Comparison in terms of AUC/ACC on Industrial dataset. Two profiling prediction tasks and three category preference identification tasks are evaluated. The best score is bold.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review, the claim "Experiments on several benchmark datasets show that our approach outperforms state-of-the-art unsupervised representation methods in downstream tasks" is moderately overstated. While LURM does show improvements over other methods in many cases, there are several considerations:

1. On the Amazon dataset (Table 2), LURM shows consistent but modest improvements (3.23%/1.49% over SUMN).

2. On the Industrial dataset (Table 3), BoI outperforms LURM in several metrics (e.g., Age prediction and some category preferences).

3. The claim doesn't acknowledge these limitations or specify the magnitude of improvements, which vary significantly across tasks.

4. The term "state-of-the-art" is broad, and while comparisons are made with several methods, it's not clear if these represent all relevant SOTA approaches.

The evidence does support LURM's general effectiveness, but the claim would be better stated with more specific performance details and acknowledgment of comparative limitations.

<score>3</score>