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:

Considering the consistency between user behaviors in different time periods, we introduce a contrastive loss function to the self-supervised training of 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:Gu et al. (2020) proposed a network named self-supervised user modeling network (SUMN) to encode user behavior data into universal representation. They introduced a behavior consistency loss, which guided the model to fully identify and preserve valuable user information under a self-supervised learning framework.

Previous works have shown that considering long-term historical behavior sequences for user modeling can significantly improve the performance of different tasks

Ren _et al._ proposed a hierarchical periodic memory network for lifelong sequential modeling. 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.

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).

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.

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features. The lifelong time \(\mathbf{T}\) is divided into \(N\) parts at a fixed time interval, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\) and \(\mathbf{t_{i}}\) denotes the \(i\)-th time period.

In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\).

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.

In this way, we can learn different aspects of user preferences.

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 \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

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.

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 \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\] The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

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).

We constructed a training dataset \(D_{pt}^{I}\) for self-supervised user modeling by randomly sampling hundred million of users who have behaviors between 2016-06 and 2021-05, while taking behaviors occurred between 2021-07 and 2021-08 to form datasets \(\{D_{ds}^{I}(t)\}_{t}\).

From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

It can be seen that the richer the behavior, the better the performance on user profiling prediction tasks. Compared to using only behaviors of two months, an average improvement of 9.75%/16.03% can be achieved when using behaviors of 5 years on these tasks. While the benefits of using richer behavioral data become small for category preference identification tasks. This is because these tasks are more focused on the short-term interests of users, and it can be observed that the best performance can be obtained when input with a length of one year is used on these tasks.

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

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

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

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

Input: 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 about introducing a contrastive loss function to consider behavior consistency appears to be somewhat overstated. While the paper does implement contrastive learning, the reviewers point out that similar contrastive approaches have been used before in other works (reviewer point #1 mentions "A very similar contrastive model pertaining approach is also studied in [4]"). The evidence shows they use standard contrastive loss formulations similar to SimCLR, rather than a novel consistency-focused adaptation. The claim implies more innovation in the contrastive learning aspect than is supported by the evidence.

<score>3</score>

Justification:
1. The evidence shows they implement standard contrastive learning between:
- Positive pairs drawn from same user behavior sequences within a time window β
- Negative pairs from other users' sequences
- Using standard InfoNCE-style loss formulation

2. The reviewer feedback indicates:
- Similar contrastive approaches already exist in prior work [4]
- The contributions are "somewhat incremental"
- The method builds on existing multi-aspect attention concepts

3. While the implementation is sound, the claim overstates the novelty of introducing contrastive learning specifically for behavior consistency, when it appears to use standard contrastive formulations without significant adaptation for this purpose.

4. The evidence does show good experimental results but doesn't strongly demonstrate that the contrastive component specifically drives the performance gains versus other aspects of the architecture.


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:

Considering the consistency between user behaviors in different time periods, we introduce a contrastive loss function to the self-supervised training of 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:Gu et al. (2020) proposed a network named self-supervised user modeling network (SUMN) to encode user behavior data into universal representation. They introduced a behavior consistency loss, which guided the model to fully identify and preserve valuable user information under a self-supervised learning framework.

Previous works have shown that considering long-term historical behavior sequences for user modeling can significantly improve the performance of different tasks

Ren _et al._ proposed a hierarchical periodic memory network for lifelong sequential modeling. 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.

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).

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.

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features. The lifelong time \(\mathbf{T}\) is divided into \(N\) parts at a fixed time interval, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\) and \(\mathbf{t_{i}}\) denotes the \(i\)-th time period.

In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\).

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.

In this way, we can learn different aspects of user preferences.

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 \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

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.

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 \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\] The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

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).

We constructed a training dataset \(D_{pt}^{I}\) for self-supervised user modeling by randomly sampling hundred million of users who have behaviors between 2016-06 and 2021-05, while taking behaviors occurred between 2021-07 and 2021-08 to form datasets \(\{D_{ds}^{I}(t)\}_{t}\).

From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

It can be seen that the richer the behavior, the better the performance on user profiling prediction tasks. Compared to using only behaviors of two months, an average improvement of 9.75%/16.03% can be achieved when using behaviors of 5 years on these tasks. While the benefits of using richer behavioral data become small for category preference identification tasks. This is because these tasks are more focused on the short-term interests of users, and it can be observed that the best performance can be obtained when input with a length of one year is used on these tasks.

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

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

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

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

Input: 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 claim and evidence, this appears to be a somewhat overstated claim. While the evidence does show that contrastive loss is implemented in SMEN's architecture for self-supervised training, the "consistency between user behaviors in different time periods" aspect is not clearly demonstrated or explained. The technical details provided focus more on the general contrastive learning implementation rather than specifically addressing temporal consistency. The review comments also highlight concerns about how the method handles temporal characteristics, noting that the time aggregation module may erase macroscopic features.

<score>3</score>

Justification:
1. The evidence shows contrastive loss is used for both item embedding and user representation learning, but doesn't clearly justify its effectiveness for temporal consistency.
2. The reviewer points out that "the time aggregation module erases macroscopic features" and questions the handling of time-series characteristics.
3. While experimental results show improvements, they don't specifically validate the temporal consistency claims.
4. The technical description focuses more on general contrastive learning mechanics rather than demonstrating how it captures temporal consistency.
5. Direct citations show basic temporal handling through monthly/yearly aggregation, but don't demonstrate sophisticated modeling of temporal consistency.


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:

With the designs above, SMEN achieves almost lossless dimensionality reduction.

The evidence to be evaluated is: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. 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'.

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.

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 \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

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.

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:
Let me assess the claim "SMEN achieves almost lossless dimensionality reduction" based on the provided evidence:

The evidence shows that SMEN performs dimensionality reduction through a multi-anchor module and time aggregation module, converting high-dimensional BoI features into lower-dimensional representations. While the text mentions "experiments prove that SMEN can achieve almost lossless dimensionality reduction" and provides technical details of the architecture, it lacks:

1. Quantitative metrics comparing information preservation before and after reduction
2. Specific experimental results demonstrating the "lossless" nature
3. Clear definition of what constitutes "almost lossless"
4. Comparative analysis showing superiority over other dimensionality reduction methods

The evidence suggests SMEN performs well, but the claim of "almost lossless" appears to be an overstatement without sufficient quantitative support.

<score>4</score>

Justification:
- The claim uses strong language ("almost lossless") without providing concrete metrics or thresholds
- While the architecture is well-described, actual experimental results showing information preservation are not presented
- The statement "experiments prove" is used without showing the actual experimental data
- The comparative statements are qualitative rather than quantitative
- The evidence does mention "similar results compared with BoI" but doesn't quantify the similarity or loss
- The technical implementation is detailed but doesn't directly support the "almost lossless" claim


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:

With the designs above, SMEN achieves almost lossless dimensionality reduction.

The review comment to be evaluated is:

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

The evidence to be evaluated is:In 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. 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'.

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.

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 \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

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.

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

Output:
After reviewing the claim and evidence, I find the claim "SMEN achieves almost lossless dimensionality reduction" to be moderately overstated. While the evidence shows SMEN performs well in dimensionality reduction through its multi-anchor module, several limitations exist:

1. The evidence only compares SMEN to limited baselines (LURM variants)
2. The review points out that multi-scale aggregation has limitations in exploring information between different time granularities
3. The industrial dataset results show BOI outperforming LURM in some cases, contradicting the "lossless" claim
4. The authors themselves acknowledge that results may be dependent on item embedding quality
5. The term "almost lossless" implies near-perfect preservation of information, which isn't conclusively demonstrated across all scenarios

<score>4</score>

The evidence shows SMEN is effective but calling it "almost lossless" overstates the actual demonstrated capabilities and ignores important limitations noted in both the evidence and review comments.


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:

With the designs above, SMEN achieves almost lossless dimensionality reduction.

The review comment to be evaluated is:

Strengths:

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

Weaknesses:

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

Minor:

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

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

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

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

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

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

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

The evidence to be evaluated is:In 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. 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'.

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.

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 \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

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.

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

Output:
After reviewing the claim and evidence, I find this claim to be moderately overstated. While the evidence shows SMEN achieves good dimensionality reduction performance, several issues make the "almost lossless" claim problematic:

1. The evidence lacks quantitative metrics specifically demonstrating "lossless" compression
2. The text acknowledges variability in performance ("LURM achieved similar results compared with BoI")
3. The reviewer notes concerns about arbitrary hyperparameter selection and insufficient experimental details
4. The claim lacks acknowledgment of limitations or conditions under which this performance is achieved
5. The evidence is largely qualitative rather than providing concrete measurements of information preservation

A more precise claim would specify the conditions and degree of performance achieved.

<score>4</score>


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

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

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

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

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

The claim to be assessed is:

With the designs above, SMEN achieves almost lossless dimensionality reduction.

The review comment to be evaluated is:

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

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

The evidence to be evaluated is:In 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. 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'.

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.

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 \(\mathbf{\tilde{R}}=\big{\{}\mathbf{\tilde{r}^{1}},\mathbf{\tilde{r}^{2}},...,\mathbf{\tilde{r }^{M}}\big{\}}\).

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.

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

Output:
After reviewing the claim and evidence, I find this claim to be overstated for several reasons:

1. While the evidence shows SMEN achieves comparable results to BoI in some cases, the term "almost lossless" is too strong given:
- The evidence only shows comparative performance, not actual loss measurements
- The reviewer points out that the method dismisses both microscopic and macroscopic time-series characteristics
- The experimental evaluations are criticized as "too easy and not suitable"
- The paper itself expresses uncertainty with phrases like "it may be that..."

2. The claim lacks precise quantification of what constitutes "almost lossless" and under what conditions this performance is achieved.

3. The reviewer's comments highlight significant limitations in handling temporal characteristics and life-long learning aspects that could result in information loss.

<score>4</score>


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

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

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

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

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

The claim to be assessed is:

In this study, we propose a novel framework called Lifelong User Representation Model (LURM) to tackle this challenge.

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

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. Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features. The lifelong time \(\mathbf{T}\) is divided into \(N\) parts at a fixed time interval, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\) and \(\mathbf{t_{i}}\) denotes the \(i\)-th time period. In our experiments, the time interval is usually set to be monthly seasonally/yearly granularity. In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\). After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations.

Details of the model will be described in the following subsections.

To capture diverse aspects of user preferences, a novel multi-anchor module is proposed.

We compared the proposed LURM model against a rich set of user representation learning methods, including TF-IDF, Doc2Vec, TextCNN, HAN, PTUM and SUMN.

Note that LURM, SUMN and PUTM were all not fine-tuned for downstream tasks 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. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks.

From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

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

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

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

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

Input: 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 3: Comparison of LURM with inputs of different lengths on several downstream tasks. The two figures show the results in terms of AUC and ACC respectively.

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 "In this study, we propose a novel framework called Lifelong User Representation Model (LURM) to tackle this challenge" and the provided evidence:

This claim is well-stated (Score 1) as it accurately represents what the paper demonstrates without exaggeration. The evidence strongly supports this by:
1. Providing detailed technical description of the LURM framework and its components (BoI and SMEN)
2. Showing comprehensive experimental results in Tables 2 and 3 comparing LURM against multiple baselines
3. Demonstrating improved performance across different tasks and datasets
4. Including clear architectural diagrams explaining the framework's design
5. Avoiding overstatement about capabilities while focusing on factual description

The claim is straightforward, factual, and directly supported by substantial evidence.

<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:

In this study, we propose a novel framework called Lifelong User Representation Model (LURM) to tackle this challenge.

The review comment to be evaluated is:

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

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

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. Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features. The lifelong time \(\mathbf{T}\) is divided into \(N\) parts at a fixed time interval, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\) and \(\mathbf{t_{i}}\) denotes the \(i\)-th time period. In our experiments, the time interval is usually set to be monthly seasonally/yearly granularity. In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\). After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations.

Details of the model will be described in the following subsections.

To capture diverse aspects of user preferences, a novel multi-anchor module is proposed.

We compared the proposed LURM model against a rich set of user representation learning methods, including TF-IDF, Doc2Vec, TextCNN, HAN, PTUM and SUMN.

Note that LURM, SUMN and PUTM were all not fine-tuned for downstream tasks 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. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks.

From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

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

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

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

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

Input: 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 3: Comparison of LURM with inputs of different lengths on several downstream tasks. The two figures show the results in terms of AUC and ACC respectively.

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

Output:
After reviewing the claim and evidence, this appears to be a moderately well-stated claim. The evidence demonstrates that LURM is indeed a novel framework with two key components (BoI and SMEN) designed to handle lifelong user behavior sequences. The experimental results in Tables 2-3 and performance comparisons show LURM generally outperforming baseline methods on multiple tasks. However, the review comments highlight some important limitations - including mixed performance benefits on certain tasks and lack of experimental validation for some components. The claim itself is appropriately scoped as simply proposing the framework without overstating its capabilities.

<score>2</score>

Justification:
1. The claim modestly states "we propose" without making grand performance claims
2. Evidence confirms LURM is indeed a novel framework with documented components
3. Tables 2-3 show competitive performance, validating basic framework viability
4. Review weaknesses highlight limitations but don't invalidate the basic claim:
- Gaps in experimental validation
- Mixed performance benefits across tasks
- Some design choices lacking clear justification
5. The claim could be even more precise by mentioning the two-component nature (BoI+SMEN), but overall maintains appropriate scope for an introductory statement


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:

In this study, we propose a novel framework called Lifelong User Representation Model (LURM) to tackle this challenge.

The review comment to be evaluated is:

Strengths:

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

Weaknesses:

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

Minor:

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

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

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

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

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

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

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

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

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. Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features. The lifelong time \(\mathbf{T}\) is divided into \(N\) parts at a fixed time interval, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}\) and \(\mathbf{t_{i}}\) denotes the \(i\)-th time period. In our experiments, the time interval is usually set to be monthly seasonally/yearly granularity. In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\). After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations.

Details of the model will be described in the following subsections.

To capture diverse aspects of user preferences, a novel multi-anchor module is proposed.

We compared the proposed LURM model against a rich set of user representation learning methods, including TF-IDF, Doc2Vec, TextCNN, HAN, PTUM and SUMN.

Note that LURM, SUMN and PUTM were all not fine-tuned for downstream tasks 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. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks.

From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

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

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

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

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

Input: 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 3: Comparison of LURM with inputs of different lengths on several downstream tasks. The two figures show the results in terms of AUC and ACC respectively.

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

Output:
After reviewing the claim, evidence, and reviewer comments, I'll evaluate whether the statement "we propose a novel framework called LURM" is well-stated or overstated.

The claim requires careful evaluation because of the use of the word "novel." Based on the evidence:

Supporting novelty:
- The framework shows superior performance on multiple tasks
- Introduces a unique two-part architecture (BoI + SMEN)

Against novelty:
- Reviewer points out that multi-anchor attention is similar to existing "multi-aspect attention" approaches
- Similar contrastive model approaches exist in prior work
- Some core components are adaptations of existing techniques

While LURM does provide improvements in performance and has some novel architectural elements, calling it entirely "novel" is somewhat overstated given the reviewer's valid points about similar existing approaches. A more accurate claim would acknowledge building upon existing techniques while highlighting the specific innovations.

<score>3</score>