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:

To meet the needs of extremely long sequence modeling, we first introduce a model named Bag of Interests (BoI) to summarize items in behavior sequences similar to Bag of Visual Words.

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 was set to \(10^{5}\), and the number of anchors \(M\) was set to 10 in our experiments.

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

In particular, it can be observed that LURM has a significant improvement over BoI.

In this table, BoI and LURM using 2-month input are compared.

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.

In this work, a novel framework named LURM is proposed to model lifelong user behaviors with any length.

Unfortunately, these methods can only process user behavior sequences with a length of hundreds, and cannot leverage the rich information brought by lifelong user behaviors. Previous works have shown that considering long-term historical behavior sequences for user modeling can significantly improve the performance of different tasks

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).

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity. Inspired by Bag of Visual Words (BoVW) Fei-Fei & Perona (2005), item is the natural granularity for aggregation.

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity. Every 'interest' is a cluster of similar items and represents a certain kind of preference.

As shown in Fig. 1 (a), BoI consists of an item embedding module and a large-scale clustering module.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model. Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together.

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'. After clustering, the cluster centers \(\mathbf{C}\) make up an 'interest' vocabulary. Therefore each item can be attributed to one/multiple 'interest(s)' by hard/soft cluster assignment. Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature \(\mathbf{b_{t}}\in\mathbb{R}^{D}\) in time period \(\mathbf{t}\) according to his/her behavior sequence \(\mathbf{S_{t}}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathbf{S_{t}}|}}\big{\}}\) : \[\mathbf{b_{t}}=[log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{1}}),log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c_{2}}), \tag{4}\] \[...,log(1+\Sigma_{i=1}^{|\mathbf{S_{t}}|}\mathbbm{1}_{\mathbf{x_{i}}\in c _{D}})]\]

As mentioned above, a BoI feature \(\mathbf{b_{t}}\) for each user is obtained, through the BoI model given behavior data in any time period \(\mathbf{t}\). Most directly, a user representation with super-high dimension \(\mathbf{b_{T}}\) can be obtained by applying the BoI model to the whole lifelong time \(\mathbf{T}\).

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time.

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 \(\mathbf{t_{i}}\).

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

\[\mathbf{r^{i}}=ReLU(\mathbf{\alpha_{i}^{T}}f(\mathbf{b}))=ReLU(\sum_{j}^{D}\alpha_{ij}f(b_ {j}))=ReLU(\sum_{j}^{D}\alpha_{ij}b_{j}\mathbf{W_{j}^{e}}), \tag{5}\] where \(f(b_{j})=b_{j}\mathbf{W_{j}^{e}}\) is the 'interest' embedding function, \(b_{j}\) is the \(j\)-th element of \(\mathbf{b}\), \(\mathbf{W^{e}}=\left(\mathbf{W_{1}^{e}},\mathbf{W_{2}^{e}},...,\mathbf{W_{D}^{e}}\right)^{T}\) is the embedding matrix.

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

Generally, user behavior in this work is aggregated monthly and yearly, i.e. \(\mathbf{T}=\{\mathbf{t_{1}},\mathbf{t_{2}},...,\mathbf{t_{N}}\}=\big{\{}\mathbf{t_{1}^{\prime}},\bm {t_{2}^{\prime}},...,\mathbf{t_{N^{\prime}}^{\prime}}\big{\}}\) (e.g., if the length of time period is 5 years, \(\mathbf{N}\) is 60 for monthly granularity, and \(\mathbf{N^{\prime}}\) is 5 for yearly granularity). Thus two sequences of BoI feature \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) and \(\mathbf{B^{\prime}}=\big{\{}\mathbf{b_{t_{1}^{\prime}}},\mathbf{b_{t_{2}^{\prime}}},...,\bm {b_{t_{N^{\prime}}^{\prime}}}\big{\}}\) at different scales correspondingly are obtained.

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

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

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

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

Output:
I'll evaluate the claim "To meet the needs of extremely long sequence modeling, we first introduce a model named Bag of Interests (BoI) to summarize items in behavior sequences similar to Bag of Visual Words."

Based on the evidence and reviewer comments, this claim appears overstated. While the BoI model does adapt the Bag of Visual Words concept to summarize user behaviors, the reviewer correctly points out that this is a standard approach already used in multiple fields. The evidence shows that BoI is primarily a clustering-based aggregation method, and doesn't contain novel technical contributions specifically for handling extremely long sequences. The reviewer also notes that the method dismisses both microscopic and macroscopic time-series characteristics.

<score>4</score>

Key evidence supporting this assessment:
1. Reviewer explicitly states "bag-of-features representation seems to be standard in several research fields"
2. Evidence shows BoI is essentially a clustering approach using existing techniques (HDSC algorithm)
3. The model uses basic time aggregation (monthly/yearly) rather than specialized long sequence handling
4. No clear technical innovation is demonstrated for extremely long sequence modeling beyond standard clustering and aggregation

The claim overstates the novelty and capability of BoI for long sequence modeling by implying it's a novel solution when it's largely an application of existing techniques.


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 way, we can use a sparse vector with super-high dimension to represent user behavior in any time period.

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.

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity. Every 'interest' is a cluster of similar items and represents a certain kind of preference. The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model. Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together.

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).

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

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time.

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.

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

For each user, the reviewed product titles constitute a sequence of review behaviors. The dataset for self-supervised user modeling on Amazon \(D_{pt}^{A}\) was constructed by collecting users who have behaviors between 1997-01 and 2017-12, while taking behaviors occurred between 2018-01 and 2018-10 to form datasets \(\{D_{ds}^{A}(t)\}_{t}\) (\(t\) refers to tasks) for downstream tasks.

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

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

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.

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. 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.

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: "In this way, we can use a sparse vector with super-high dimension to represent user behavior in any time period."

Analysis:
The evidence strongly supports this claim through:
1. The implementation of Bag of Interests (BoI) model which creates sparse high-dimensional vectors (dimension ~10^5) to represent user interests
2. Concrete mathematical formulation showing how the sparse vector b_t is constructed for any time period t
3. Demonstration of application across different time granularities (monthly and yearly)
4. Practical implementation showing the method works with real user behavior data from 1997-2017

The claim is well-stated as it:
- Accurately describes the vector as "sparse" and "super-high dimension"
- Correctly indicates it can represent "any time period"
- Doesn't overreach beyond what the evidence demonstrates
- Is supported by detailed technical implementation details and experimental validation

<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 way, we can use a sparse vector with super-high dimension to represent user behavior in any time period.

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.

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity. Every 'interest' is a cluster of similar items and represents a certain kind of preference. The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model. Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together.

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).

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

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time.

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.

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

For each user, the reviewed product titles constitute a sequence of review behaviors. The dataset for self-supervised user modeling on Amazon \(D_{pt}^{A}\) was constructed by collecting users who have behaviors between 1997-01 and 2017-12, while taking behaviors occurred between 2018-01 and 2018-10 to form datasets \(\{D_{ds}^{A}(t)\}_{t}\) (\(t\) refers to tasks) for downstream tasks.

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

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

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.

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. 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.

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

Output:
I've carefully reviewed the claim "we can use a sparse vector with super-high dimension to represent user behavior in any time period" against the provided evidence and review comments.

Summary of evaluation:
The evidence strongly supports this claim through:
1. Clear demonstration of the BoI model that creates sparse high-dimensional vectors (dimension ~10^5) to represent user interests
2. Detailed mathematical formulation showing how behaviors in any time period t can be represented (equation 4)
3. Evidence that the model works across different time granularities (monthly and yearly)
4. Practical implementation showing effectiveness with real data spanning multiple years

While some weaknesses were noted in the review comments (particularly around multi-scale aggregation), these don't directly challenge the core claim about the ability to represent behavior using sparse high-dimensional vectors. The claim is measured and accurately reflects what the evidence demonstrates.

<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 way, we can use a sparse vector with super-high dimension to represent user behavior in any time period.

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.

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity. Every 'interest' is a cluster of similar items and represents a certain kind of preference. The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model. Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together.

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).

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

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time.

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.

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

For each user, the reviewed product titles constitute a sequence of review behaviors. The dataset for self-supervised user modeling on Amazon \(D_{pt}^{A}\) was constructed by collecting users who have behaviors between 1997-01 and 2017-12, while taking behaviors occurred between 2018-01 and 2018-10 to form datasets \(\{D_{ds}^{A}(t)\}_{t}\) (\(t\) refers to tasks) for downstream tasks.

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

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

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.

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. 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.

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 "we can use a sparse vector with super-high dimension to represent user behavior in any time period" based on the provided evidence:

The evidence shows that the authors:
1. Use a Bag of Interests (BoI) model with vocabulary size of 10^5 to create sparse high-dimensional vectors
2. Demonstrate the method works across different time periods (monthly and yearly granularities)
3. Present formulas showing how behavior sequences are converted to sparse vectors
4. Show experimental results over a 20-year period (1997-2017)

However, the reviewer points out:
1. Insufficient comparison with other universal user modeling methods
2. Questions about generalizability to other types of user models
3. Arbitrary selection of some hyperparameters

While the evidence supports the core claim about using sparse vectors to represent behavior across time periods, the "any time period" part may be slightly overstated since testing was limited to specific granularities and timeframes.

<score>2</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 way, we can use a sparse vector with super-high dimension to represent user behavior in any time period.

The review comment to be evaluated is:

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

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

The evidence to be evaluated is:In this work, we are committed to learning universal user representation through truly lifelong user behavior sequences with arbitrary length.

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at 'interest' granularity. Every 'interest' is a cluster of similar items and represents a certain kind of preference. The size of 'interest' vocabulary is often selected at a level of about \(10^{5}\) for retaining enough details.

Like the BoVW model, an 'interest' vocabulary are supposed to be built in our BoI model. Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together.

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).

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

Therefore, we propose to get a BoI feature sequence by applying the BoI model at each time period of the whole lifelong time.

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.

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

For each user, the reviewed product titles constitute a sequence of review behaviors. The dataset for self-supervised user modeling on Amazon \(D_{pt}^{A}\) was constructed by collecting users who have behaviors between 1997-01 and 2017-12, while taking behaviors occurred between 2018-01 and 2018-10 to form datasets \(\{D_{ds}^{A}(t)\}_{t}\) (\(t\) refers to tasks) for downstream tasks.

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

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

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.

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. 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.

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

Output:
I'll assess the claim: "In this way, we can use a sparse vector with super-high dimension to represent user behavior in any time period."

1. Summary Evaluation:
The claim is somewhat overstated. While the evidence shows they do use sparse high-dimensional vectors (BoI features) to represent user behaviors in different time periods, there are several limitations:
- The reviewer points out the method dismisses both microscopic and macroscopic time-series characteristics
- The approach has limitations in handling truly "any" time period, as the experiments only covered specific granularities (monthly and yearly)
- The technique uses a fixed vocabulary size (10^5) and specific clustering approach, which may not generalize to all scenarios
- The experimental validation is limited in scope, as noted by the reviewer

2. <score>3</score>

Justification:
- The evidence does support that they use sparse vectors through the BoI model, with vocabulary size of 10^5 and equation (4) showing the sparse representation
- However, the claim overstates the universality ("any time period") when:
  - The evidence shows they specifically used monthly and yearly granularities
  - The reviewer highlights limitations in handling both microscopic and macroscopic temporal features
  - The experiments are limited in scope and "too easy" according to the reviewer
  - There are technical constraints (fixed vocabulary size, specific clustering approach) that aren't acknowledged in the broad claim
- The claim would be better stated with qualifications about the specific conditions and limitations under which their approach works.


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:

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) that maps sequences of BoI features to multiple low-dimensional user representations is proposed.

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).

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

As shown in Fig. 1 (a), BoI consists of an item embedding module and a large-scale clustering module.

Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016). Given a set of users \(U=\{u_{1},u_{2},...,u_{|U|}\}\), each user \(u\in U\) corresponds to a behavior sequence \(\mathbf{S}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathcal{S}|}}\big{\}}\), where \(\mathbf{x_{i}}\in\mathbf{S}\) denotes the \(i\)-th item. \(|\mathbf{U}|\) and \(|\mathbf{S}|\) denote the number of users and the length of \(u\)'s behaviors respectively. Generally, the content of an item \(\mathbf{x}\) can be expressed as \(\{w_{1},w_{2},...,w_{|\mathbf{x}|}\}\), where \(w_{i}\) denotes a word from a vocabulary \(V\), and \(|\mathbf{x}|\) denotes the number of words in the content of \(\mathbf{x}\). Firstly, an encoder with average operation is used to generate item embedding \(c\):

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

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.

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. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

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

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

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

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

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

The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\]

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

The entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval.

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

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

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

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation. In this work, a novel framework named LURM is proposed to model lifelong user behaviors with any length.

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

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

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

Output:
After reviewing the claim and evidence, I find the claim about SMEN to be well-stated and accurately reflects what is presented in the evidence. The evidence thoroughly describes SMEN's architecture and functionality, showing how it maps BoI feature sequences to multiple low-dimensional user representations through its multi-anchor module, time aggregation module, and multi-scale aggregation components. The technical details, mathematical formulations, and architectural diagrams (especially Figure 1b and Figure 2) directly support the claim's description. The evidence demonstrates that SMEN indeed performs the claimed mapping function without exaggeration.

<score>1</score>

Justification:
1. The evidence provides detailed mathematical formulations (equations 4-8) explaining how SMEN processes and maps the BoI features
2. The architectural diagrams (Figures 1 and 2) clearly illustrate the mapping process
3. The evidence confirms that SMEN maps sequences to multiple representations ("outputs a group of diverse representations")
4. The evidence shows that the representations are indeed low-dimensional (compressed) through statements about "lossless dimensionality reduction"
5. No instances of overstatement or exaggeration are found - the claim aligns precisely with what is demonstrated in the evidence


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

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

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

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

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

The claim to be assessed is:

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) that maps sequences of BoI features to multiple low-dimensional user representations is proposed.

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).

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

As shown in Fig. 1 (a), BoI consists of an item embedding module and a large-scale clustering module.

Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016). Given a set of users \(U=\{u_{1},u_{2},...,u_{|U|}\}\), each user \(u\in U\) corresponds to a behavior sequence \(\mathbf{S}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathcal{S}|}}\big{\}}\), where \(\mathbf{x_{i}}\in\mathbf{S}\) denotes the \(i\)-th item. \(|\mathbf{U}|\) and \(|\mathbf{S}|\) denote the number of users and the length of \(u\)'s behaviors respectively. Generally, the content of an item \(\mathbf{x}\) can be expressed as \(\{w_{1},w_{2},...,w_{|\mathbf{x}|}\}\), where \(w_{i}\) denotes a word from a vocabulary \(V\), and \(|\mathbf{x}|\) denotes the number of words in the content of \(\mathbf{x}\). Firstly, an encoder with average operation is used to generate item embedding \(c\):

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

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.

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. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

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

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

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

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

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

The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\]

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

The entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval.

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

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

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

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation. In this work, a novel framework named LURM is proposed to model lifelong user behaviors with any length.

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

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

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

Output:
Let me assess the claim: "Then, a Self-supervised Multi-anchor Encoder Network (SMEN) that maps sequences of BoI features to multiple low-dimensional user representations is proposed."

1. Summary of evaluation:
The evidence thoroughly supports this claim through detailed technical descriptions and figures. The evidence shows that SMEN takes BoI feature sequences as input and maps them to low-dimensional representations through multiple components (multi-anchor module, time aggregation module, multi-scale aggregation module). Figure 1(b) provides a clear visualization of this architecture, and Figure 2 details the multi-anchor module's operation. The claim is well-stated and accurately reflects what SMEN does without exaggeration.

<score>1</score>

Justification:
1. The evidence directly demonstrates how SMEN maps BoI features to representations:
- Shows detailed mathematical formulations (equations 5-8)
- Provides architectural diagrams (Figures 1 and 2)
- Explains the complete workflow through multiple modules

2. The claim makes no exaggerated assertions:
- Accurately describes SMEN's core function
- Uses precise technical language
- Matches the implementation details provided in evidence

3. The reviewer comments don't dispute this specific claim about SMEN's basic functionality, though they raise other concerns about experimental validation and inter-granularity information usage.

The claim earns a score of 1 as it precisely describes SMEN's fundamental purpose and operation without overstating its capabilities or effectiveness.


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:

Then, a Self-supervised Multi-anchor Encoder Network (SMEN) that maps sequences of BoI features to multiple low-dimensional user representations is proposed.

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).

In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.

As shown in Fig. 1 (a), BoI consists of an item embedding module and a large-scale clustering module.

Inspired by these works, we design a contrastive learning task based on the relation between items drawn from a user to learn item embedding similar to item2vec Barkan & Koenigstein (2016). Given a set of users \(U=\{u_{1},u_{2},...,u_{|U|}\}\), each user \(u\in U\) corresponds to a behavior sequence \(\mathbf{S}=\big{\{}\mathbf{x_{1}},\mathbf{x_{2}},...,\mathbf{x_{|\mathcal{S}|}}\big{\}}\), where \(\mathbf{x_{i}}\in\mathbf{S}\) denotes the \(i\)-th item. \(|\mathbf{U}|\) and \(|\mathbf{S}|\) denote the number of users and the length of \(u\)'s behaviors respectively. Generally, the content of an item \(\mathbf{x}\) can be expressed as \(\{w_{1},w_{2},...,w_{|\mathbf{x}|}\}\), where \(w_{i}\) denotes a word from a vocabulary \(V\), and \(|\mathbf{x}|\) denotes the number of words in the content of \(\mathbf{x}\). Firstly, an encoder with average operation is used to generate item embedding \(c\):

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

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.

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. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

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

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

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

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

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

The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\]

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

The entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval.

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

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

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

And It can be seen that the representation was improved through the further learning of SMEN with self-supervised tasks and the ability to encode temporal variation. In this work, a novel framework named LURM is proposed to model lifelong user behaviors with any length.

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

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

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

Output:
Based on the evidence provided, I'll evaluate the claim "Then, a Self-supervised Multi-anchor Encoder Network (SMEN) that maps sequences of BoI features to multiple low-dimensional user representations is proposed."

The evidence strongly supports this claim through:
1. Detailed technical description of SMEN's architecture and functionality
2. Clear illustrations in Figures 1 and 2 showing how SMEN processes BoI features
3. Mathematical formulations demonstrating the mapping process
4. Explicit explanation of how SMEN converts BoI features into multiple user representations through its multi-anchor module

Even the reviewer comments, while critical of other aspects, don't dispute this basic architectural claim. The claim is factual and accurately reflects what SMEN does without exaggeration.

<score>1</score>