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:

* We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN.

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:They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi _et al._ decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally.

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).

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.

After the encoder above has been trained, an item embedding set \(\mathbf{E}=\{\mathbf{e_{i}}\}_{i\in I}\) is obtained, where \(I\) is the complete collection of items at the billion level.

Considering the large-scale item set, a subset \(\mathbf{E^{\prime}}\subset\mathbf{E}\) at the million level is sampled, and an efficient clustering algorithm named HDSC Yi et al. (2014) on \(\mathbf{E^{\prime}}\) is employed to cluster similar items into the same 'interest'. After clustering, the cluster centers \(\mathbf{C}\) make up an 'interest' vocabulary. Therefore each item can be attributed to one/multiple 'interest(s)' by hard/soft cluster assignment. Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\]

However, there are two main disadvantages: 1) it is not friendly to the downstream tasks since the dimension of representation is too high, and 2) it is too crude to aggregate the information in the whole lifelong time without considering variations over time.

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 \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

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.

\[\mathbf{\hat{r}^{i}}=s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}])\odot \mathbf{\tilde{r}^{i}}+(1-s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}]) )\odot\mathbf{\tilde{r^{\prime}}^{i}}, \tag{7}\] where \(\odot\) is the Hadamard product, \([\cdot]\) is a operation concatenating vectors along the last dimension, and \(s^{i}(\cdot)\) is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. 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 entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval.

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 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 my review of the evidence and reviewer comments, the claim "We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN" requires careful evaluation.

While the evidence shows that SMEN achieves dimensionality reduction through its multi-anchor module and demonstrates some effectiveness, there are important limitations:

1. The reviewer points out that the benefits on category preference tasks are minimal, even with 5 years of behavior data.
2. On the industrial dataset, BoI performed better than LURM, suggesting SMEN's benefits aren't consistent.
3. The multi-scale aggregation module's effectiveness lacks experimental validation.
4. The claim of "little information loss" is not fully substantiated across all scenarios.

The claim somewhat overstates the model's capabilities given these limitations, though there is evidence of some successful compression.

<score>3</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:

* We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN.

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:They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi _et al._ decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally.

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).

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.

After the encoder above has been trained, an item embedding set \(\mathbf{E}=\{\mathbf{e_{i}}\}_{i\in I}\) is obtained, where \(I\) is the complete collection of items at the billion level.

Considering the large-scale item set, a subset \(\mathbf{E^{\prime}}\subset\mathbf{E}\) at the million level is sampled, and an efficient clustering algorithm named HDSC Yi et al. (2014) on \(\mathbf{E^{\prime}}\) is employed to cluster similar items into the same 'interest'. After clustering, the cluster centers \(\mathbf{C}\) make up an 'interest' vocabulary. Therefore each item can be attributed to one/multiple 'interest(s)' by hard/soft cluster assignment. Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\]

However, there are two main disadvantages: 1) it is not friendly to the downstream tasks since the dimension of representation is too high, and 2) it is too crude to aggregate the information in the whole lifelong time without considering variations over time.

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 \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

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.

\[\mathbf{\hat{r}^{i}}=s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}])\odot \mathbf{\tilde{r}^{i}}+(1-s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}]) )\odot\mathbf{\tilde{r^{\prime}}^{i}}, \tag{7}\] where \(\odot\) is the Hadamard product, \([\cdot]\) is a operation concatenating vectors along the last dimension, and \(s^{i}(\cdot)\) is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. 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 entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval.

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 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 "We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN."

The evidence provides substantial technical details about SMEN's architecture and performance. The authors demonstrate that SMEN achieves "almost lossless dimensionality reduction" through:
1. Multi-anchor module design that converts high-dimensional BoI features into lower-dimensional representations
2. Experimental validation comparing LURM with BoI benchmark
3. Detailed technical explanation of the compression mechanism

However, the review points out some limitations:
- The multi-anchor attention approach is not entirely novel
- Insufficient comparison with other universal user modeling methods
- Limited verification of the method's generality

While the evidence supports the claim's core assertion about compression with minimal information loss, the claim could better acknowledge these limitations.

<score>2</score>

The claim is relatively well-stated but could be slightly more precise in acknowledging limitations and comparative context. The evidence substantially supports the technical achievement while the reviewers highlight important caveats about novelty and generalizability.


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:

* We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN.

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:They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi _et al._ decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally.

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).

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.

After the encoder above has been trained, an item embedding set \(\mathbf{E}=\{\mathbf{e_{i}}\}_{i\in I}\) is obtained, where \(I\) is the complete collection of items at the billion level.

Considering the large-scale item set, a subset \(\mathbf{E^{\prime}}\subset\mathbf{E}\) at the million level is sampled, and an efficient clustering algorithm named HDSC Yi et al. (2014) on \(\mathbf{E^{\prime}}\) is employed to cluster similar items into the same 'interest'. After clustering, the cluster centers \(\mathbf{C}\) make up an 'interest' vocabulary. Therefore each item can be attributed to one/multiple 'interest(s)' by hard/soft cluster assignment. Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\]

However, there are two main disadvantages: 1) it is not friendly to the downstream tasks since the dimension of representation is too high, and 2) it is too crude to aggregate the information in the whole lifelong time without considering variations over time.

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 \[\alpha_{ij}=\frac{\exp(\mathbf{W_{i}^{a}}\mathbf{k_{j}})}{\sum_{l}\exp(\mathbf{W_{l}^{a}} \mathbf{k_{j}})}, \tag{6}\]

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.

\[\mathbf{\hat{r}^{i}}=s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}])\odot \mathbf{\tilde{r}^{i}}+(1-s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}]) )\odot\mathbf{\tilde{r^{\prime}}^{i}}, \tag{7}\] where \(\odot\) is the Hadamard product, \([\cdot]\) is a operation concatenating vectors along the last dimension, and \(s^{i}(\cdot)\) is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. 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 entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval.

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 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 my review of the claim, evidence, and reviewer comments, the claim that "We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN" requires careful evaluation.

While the evidence shows that SMEN employs sophisticated techniques (multi-anchor module, time aggregation, etc.) to compress user representations, the reviewer raises valid concerns about:
1. Lack of novelty in the techniques used (similar to transformer's multi-head attention)
2. Loss of both microscopic and macroscopic time-series characteristics during compression
3. Limited experimental evaluation for long behavior sequences

The evidence does suggest some positive results ("almost lossless dimensionality reduction"), but lacks comprehensive validation of the "little information loss" claim across all important aspects of user behavior representation.

<score>3</score>

Justification:
1. The claim is partially supported by:
- Technical implementation details showing a well-designed compression architecture
- Some experimental results indicating successful dimensionality reduction compared to baselines

2. However, it is somewhat overstated due to:
- Insufficient evidence addressing preservation of temporal dynamics
- Reviewer-identified limitations in handling microscopic and macroscopic features
- Limited experimental validation for long-term behavior sequences
- No clear benchmarking against state-of-the-art compression methods

The claim would be better stated with qualifications about specific types of information preserved and lost during compression.


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:

(i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (_e.g._, \(10^{5}\));

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).

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity.

The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model.

In order to retain details as many as possible, the size of the 'interest' vocabulary is set to be at \(10^{4}\sim 10^{5}\) level. In other words, all items should be clustered into \(D\) (e.g., \(10^{5}\)) categories.

Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\] where \(\mathbf{x_{i}}\in c_{j}\) means item \(i\) is assigned to the cluster \(c_{j}\in\mathbf{C}\), \(\mathbbm{1}\) is an indicator function. As mentioned above, a BoI feature \(\mathbf{b_{t}}\) for each user is obtained, through the BoI model given behavior data in any time period \(\mathbf{t}\). Most directly, a user representation with super-high dimension \(\mathbf{b_{T}}\) can be obtained by applying the BoI model to the whole lifelong time \(\mathbf{T}\).

Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2.

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.

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.

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.

TF-IDF generates a sparse high-dimensional vector, while Doc2Vec learns to represent the document by a dense vector.

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.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM.

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:
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 "Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5)" appears to be well-stated and accurately reflects the evidence provided. The evidence consistently shows that:

1. BoI aggregates user behavior data at 'interest' granularity
2. The vocabulary size is explicitly set to 10^4~10^5 level "to retain enough details"
3. The model generates sparse high-dimensional vectors (confirmed by comparison to TF-IDF)
4. The method can encode behaviors from any time period, as demonstrated by the monthly and yearly aggregation examples

The claim directly aligns with the implementation details and mathematical formulations presented in the evidence without exaggeration.

<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:

(i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (_e.g._, \(10^{5}\));

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).

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity.

The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model.

In order to retain details as many as possible, the size of the 'interest' vocabulary is set to be at \(10^{4}\sim 10^{5}\) level. In other words, all items should be clustered into \(D\) (e.g., \(10^{5}\)) categories.

Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\] where \(\mathbf{x_{i}}\in c_{j}\) means item \(i\) is assigned to the cluster \(c_{j}\in\mathbf{C}\), \(\mathbbm{1}\) is an indicator function. As mentioned above, a BoI feature \(\mathbf{b_{t}}\) for each user is obtained, through the BoI model given behavior data in any time period \(\mathbf{t}\). Most directly, a user representation with super-high dimension \(\mathbf{b_{T}}\) can be obtained by applying the BoI model to the whole lifelong time \(\mathbf{T}\).

Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2.

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.

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.

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.

TF-IDF generates a sparse high-dimensional vector, while Doc2Vec learns to represent the document by a dense vector.

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.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM.

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:
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 "(i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5)" appears to be well-stated and accurately supported by the evidence.

The evidence clearly shows that:
- BoI aggregates user behavior data at 'interest' granularity
- The vocabulary size is explicitly set to 10^4~10^5 level "to retain enough details"
- The model generates sparse high-dimensional vectors (as shown in equation 4)
- The evidence demonstrates that BoI can encode behaviors from different time periods (monthly, yearly)
- The implementation details match the claim's description

The claim makes no exaggerated statements and directly reflects what is demonstrated in both the methodology and experiments.

<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:

(i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (_e.g._, \(10^{5}\));

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).

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity.

The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model.

In order to retain details as many as possible, the size of the 'interest' vocabulary is set to be at \(10^{4}\sim 10^{5}\) level. In other words, all items should be clustered into \(D\) (e.g., \(10^{5}\)) categories.

Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\] where \(\mathbf{x_{i}}\in c_{j}\) means item \(i\) is assigned to the cluster \(c_{j}\in\mathbf{C}\), \(\mathbbm{1}\) is an indicator function. As mentioned above, a BoI feature \(\mathbf{b_{t}}\) for each user is obtained, through the BoI model given behavior data in any time period \(\mathbf{t}\). Most directly, a user representation with super-high dimension \(\mathbf{b_{T}}\) can be obtained by applying the BoI model to the whole lifelong time \(\mathbf{T}\).

Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2.

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.

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.

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.

TF-IDF generates a sparse high-dimensional vector, while Doc2Vec learns to represent the document by a dense vector.

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.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM.

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:
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, the claim "(i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5)" appears to be well-stated and accurately reflects the evidence without exaggeration.

The evidence clearly shows that:
- BoI aggregates user behavior data at 'interest' granularity
- The vocabulary size is explicitly set to 10^4~10^5 level
- The model generates sparse high-dimensional vectors (shown in equation 4)
- It can encode behaviors from any time period (monthly, yearly, or whole lifelong time T)

The review comments don't dispute this specific technical claim about BoI's encoding capabilities. The concerns raised are more about the overall method's novelty and effectiveness rather than this specific characteristic of the BoI component.

<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:

(i) Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (_e.g._, \(10^{5}\));

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).

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity.

The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model.

In order to retain details as many as possible, the size of the 'interest' vocabulary is set to be at \(10^{4}\sim 10^{5}\) level. In other words, all items should be clustered into \(D\) (e.g., \(10^{5}\)) categories.

Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\] where \(\mathbf{x_{i}}\in c_{j}\) means item \(i\) is assigned to the cluster \(c_{j}\in\mathbf{C}\), \(\mathbbm{1}\) is an indicator function. As mentioned above, a BoI feature \(\mathbf{b_{t}}\) for each user is obtained, through the BoI model given behavior data in any time period \(\mathbf{t}\). Most directly, a user representation with super-high dimension \(\mathbf{b_{T}}\) can be obtained by applying the BoI model to the whole lifelong time \(\mathbf{T}\).

Let \(\mathbf{b}\) be a BoI feature, the module converts \(\mathbf{b}\) to \(M\) low-dimensional representations as shown in Fig. 2.

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.

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.

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.

TF-IDF generates a sparse high-dimensional vector, while Doc2Vec learns to represent the document by a dense vector.

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.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM.

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:
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 and reviewer comments, I'll evaluate the claim: "Bag of Interests (BoI) encodes user behaviors in any time period into a sparse vector with super-high dimension (e.g., 10^5)"

This claim appears well-stated based on the following evidence:
- Multiple references confirm the vocabulary size is set to 10^4~10^5 level
- The mathematical formulation in equation (4) shows how behaviors are encoded into sparse vectors
- The evidence explicitly states they set the vocabulary size to 10^5 in experiments
- The comparison with TF-IDF confirms the sparse nature of the vectors
- The reviewer comments don't dispute this technical aspect of the method

The claim accurately reflects the technical implementation without exaggeration and is supported by concrete evidence and mathematical formulation.

<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 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 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" appears to be well-stated. The evidence thoroughly documents how SMEN processes BoI features through multi-anchor, time aggregation, and multi-scale aggregation modules, using contrastive learning to generate low-dimensional representations. The mathematical formulations and architectural details provided in the text and figures directly support all aspects of the claim. The claim makes no exaggerated statements and accurately reflects the documented functionality.

<score>1</score>

Justification:
1. The evidence explicitly shows SMEN maps BoI features sequences through multiple processing steps (Equations 5-8)
2. The multi-anchor module is clearly demonstrated to generate multiple low-dimensional representations
3. Contrastive learning implementation is detailed mathematically
4. Figures 1 and 2 provide architectural validation
5. No overstatement of capabilities - the claim strictly describes the documented functionality
6. Supporting evidence includes working equations and implementation details
7. Results sections confirm the model performs as claimed

The claim is precise, technically accurate, and fully supported by the extensive technical evidence provided.