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 work, we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration.

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

Ren _et al._ proposed a hierarchical periodic memory network for lifelong sequential modeling. They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi _et al._ decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally. Pi _et al._ also designed a search-based interest model (SIM) with a cascaded two-stage search paradigm to capture the diverse long-term interest with target item.

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.

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

In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\). After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

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

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

Compared to average and max pooling, RNNs are more appropriate to capture variations over time.

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

\[\mathbf{\hat{r}^{i}}=s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}])\odot \mathbf{\tilde{r}^{i}}+(1-s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}]) )\odot\mathbf{\tilde{r^{\prime}}^{i}}, \tag{7}\] where \(\odot\) is the Hadamard product, \([\cdot]\) is a operation concatenating vectors along the last dimension, and \(s^{i}(\cdot)\) is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation. As mentioned in 3.1.1, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. \(h(\cdot)\), as used in SimCLR Chen et al. (2020). The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\] The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

We set the latent dimensions of word/item embedding and all hidden layers to 128 in LURM. In item embedding module, the window size \(\beta\) was set to 5 days, and the temperature \(\tau\) was 0.1. 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. In addition, the input of month-granularity and year-granularity were used.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively. Even compared with supervised methods, LURM outperforms TextCNN and HAN about 8.26%/3.47% and 3.82%/2.04% respectively. In particular, it can be observed that LURM has a significant improvement over BoI.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks. When using longer input, we can obtain more improvements, e.g., about 15.55%/17.55% average improvement on user profiling prediction tasks, and 4.19%/2.91% average improvement on category preference identification tasks compared with SUMN. From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN. Fig. 3 shows the performance of LURM on several downstream tasks of industrial dataset when using inputs of different lengths. It can be seen that the richer the behavior, the better the performance on user profiling prediction tasks. Compared to using only behaviors of two months, an average improvement of 9.75%/16.03% can be achieved when using behaviors of 5 years on these tasks. While the benefits of using richer behavioral data become small for category preference identification tasks. This is because these tasks are more focused on the short-term interests of users, and it can be observed that the best performance can be obtained when input with a length of one year is used on these tasks.

It can be seen that using multi-scale aggregation module can achieve significant improvements on user profiling prediction tasks, while the benefits disappear on category preference tasks.

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

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

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

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

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

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

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

Output:
Based on the evidence provided, the claim "In this work, we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration" appears to be well-stated. The evidence thoroughly demonstrates:

1. A detailed framework description with two sub-models (BoI and SMEN)
2. Comprehensive experimental validation showing LURM's effectiveness across multiple tasks
3. Clear technical details about the model architecture and implementation
4. Quantitative results showing improvements over baseline methods

The claim is modest and accurately reflects what the paper delivers - a new framework for modeling long-term user behaviors. It doesn't overstate capabilities or results. The reviewer comments, while noting some limitations, largely support the fundamental claim about proposing this framework.

<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 work, we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration.

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

Ren _et al._ proposed a hierarchical periodic memory network for lifelong sequential modeling. They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi _et al._ decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally. Pi _et al._ also designed a search-based interest model (SIM) with a cascaded two-stage search paradigm to capture the diverse long-term interest with target item.

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.

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

In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\). After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

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

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

Compared to average and max pooling, RNNs are more appropriate to capture variations over time.

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

\[\mathbf{\hat{r}^{i}}=s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}])\odot \mathbf{\tilde{r}^{i}}+(1-s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}]) )\odot\mathbf{\tilde{r^{\prime}}^{i}}, \tag{7}\] where \(\odot\) is the Hadamard product, \([\cdot]\) is a operation concatenating vectors along the last dimension, and \(s^{i}(\cdot)\) is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation. As mentioned in 3.1.1, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. \(h(\cdot)\), as used in SimCLR Chen et al. (2020). The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\] The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

We set the latent dimensions of word/item embedding and all hidden layers to 128 in LURM. In item embedding module, the window size \(\beta\) was set to 5 days, and the temperature \(\tau\) was 0.1. 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. In addition, the input of month-granularity and year-granularity were used.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively. Even compared with supervised methods, LURM outperforms TextCNN and HAN about 8.26%/3.47% and 3.82%/2.04% respectively. In particular, it can be observed that LURM has a significant improvement over BoI.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks. When using longer input, we can obtain more improvements, e.g., about 15.55%/17.55% average improvement on user profiling prediction tasks, and 4.19%/2.91% average improvement on category preference identification tasks compared with SUMN. From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN. Fig. 3 shows the performance of LURM on several downstream tasks of industrial dataset when using inputs of different lengths. It can be seen that the richer the behavior, the better the performance on user profiling prediction tasks. Compared to using only behaviors of two months, an average improvement of 9.75%/16.03% can be achieved when using behaviors of 5 years on these tasks. While the benefits of using richer behavioral data become small for category preference identification tasks. This is because these tasks are more focused on the short-term interests of users, and it can be observed that the best performance can be obtained when input with a length of one year is used on these tasks.

It can be seen that using multi-scale aggregation module can achieve significant improvements on user profiling prediction tasks, while the benefits disappear on category preference tasks.

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

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

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

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

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

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

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

Output:
After reviewing the evidence, the claim "we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration" appears to be somewhat overstated given the reviewer comments and evidence.

While the framework shows promising results and introduces some interesting components (multi-scale aggregation, BoI features), the reviewer points out that key elements like the multi-anchor attention network are not entirely novel and have been used in previous works. The reviewer also notes insufficient comparisons with universal user modeling methods and questions about hyperparameter selection and generalizability. The empirical evidence shows good performance but doesn't fully validate the claim of novelty.

<score>3</score>

Justification:
1. The multi-anchor attention mechanism is noted as being similar to existing "multi-aspect attention" approaches (Reviewer point 1)
2. Limited comparison with universal user modeling methods and other baselines (Reviewer points 2,4)
3. While results show improvements over baselines, some core design choices lack clear justification (Reviewer point 6)
4. Questions about generalizability to other user models (Reviewer point 7)
5. The experimental results do demonstrate effectiveness but don't fully support claims of novelty

The claim would be better stated by acknowledging the incremental nature of the contributions while highlighting the specific improvements and applications demonstrated.


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 work, we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration.

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

Ren _et al._ proposed a hierarchical periodic memory network for lifelong sequential modeling. They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi _et al._ decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally. Pi _et al._ also designed a search-based interest model (SIM) with a cascaded two-stage search paradigm to capture the diverse long-term interest with target item.

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.

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

In this way, a sequence of BoI features \(\mathbf{B}=\{\mathbf{b_{t_{1}}},\mathbf{b_{t_{2}}},...,\mathbf{b_{t_{N}}}\}\) can be obtained, where \(\mathbf{b_{t_{i}}}\) denotes the BoI feature corresponding to the \(i\)-th time period \(\mathbf{t_{i}}\). After that, SMEN is used to map \(\mathbf{B}\) to low-dimensional user representations. As shown in Fig. 1 (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.

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

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

Compared to average and max pooling, RNNs are more appropriate to capture variations over time.

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

\[\mathbf{\hat{r}^{i}}=s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}])\odot \mathbf{\tilde{r}^{i}}+(1-s^{i}([\mathbf{\tilde{r}^{i}},\mathbf{\tilde{r^{\prime}}^{i}}]) )\odot\mathbf{\tilde{r^{\prime}}^{i}}, \tag{7}\] where \(\odot\) is the Hadamard product, \([\cdot]\) is a operation concatenating vectors along the last dimension, and \(s^{i}(\cdot)\) is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. The switch function controls the fusion between BoI feature sequences of different scales. The output of this module \(\mathbf{\hat{R}}=[\mathbf{\hat{r}^{1}},\mathbf{\hat{r}^{2}},...,\mathbf{\hat{r}^{M}}]\) is served as the final user representation. As mentioned in 3.1.1, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. \(h(\cdot)\), as used in SimCLR Chen et al. (2020). The function \(h(\cdot)\) is computed as \[\mathbf{v} =ReLU([\mathbf{W_{1}^{h_{1}}}\mathbf{\hat{r}^{1}},\mathbf{W_{2}^{h_{1}}}\mathbf{ \hat{r}^{2}},...,\mathbf{W_{M}^{h_{1}}}\mathbf{\hat{r}^{M}}]) \tag{8}\] \[h(\mathbf{v}) =\mathbf{W^{h_{2}}}ReLU(\mathbf{v}+mix(\mathbf{v},\mathbf{W^{h_{2}}}).\] The function \(mix(\mathbf{v},\mathbf{W^{h_{3}}})\) is to allow interaction between different preferences, and is implemented as \[mix(\mathbf{v},\mathbf{W^{h_{3}}})=r_{2}(\mathbf{W^{h_{3}}}r_{1}(\mathbf{v})), \tag{9}\]

We set the latent dimensions of word/item embedding and all hidden layers to 128 in LURM. In item embedding module, the window size \(\beta\) was set to 5 days, and the temperature \(\tau\) was 0.1. 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. In addition, the input of month-granularity and year-granularity were used.

The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23%/1.49% average improvements than SUMN and 4.41%/2.09% average improvements than PUTM in terms of AUC and ACC respectively. Even compared with supervised methods, LURM outperforms TextCNN and HAN about 8.26%/3.47% and 3.82%/2.04% respectively. In particular, it can be observed that LURM has a significant improvement over BoI.

When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks. When using longer input, we can obtain more improvements, e.g., about 15.55%/17.55% average improvement on user profiling prediction tasks, and 4.19%/2.91% average improvement on category preference identification tasks compared with SUMN. From Table 2 and Table 3, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN. Fig. 3 shows the performance of LURM on several downstream tasks of industrial dataset when using inputs of different lengths. It can be seen that the richer the behavior, the better the performance on user profiling prediction tasks. Compared to using only behaviors of two months, an average improvement of 9.75%/16.03% can be achieved when using behaviors of 5 years on these tasks. While the benefits of using richer behavioral data become small for category preference identification tasks. This is because these tasks are more focused on the short-term interests of users, and it can be observed that the best performance can be obtained when input with a length of one year is used on these tasks.

It can be seen that using multi-scale aggregation module can achieve significant improvements on user profiling prediction tasks, while the benefits disappear on category preference tasks.

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

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

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

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

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

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

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

Output:
After reviewing the evidence and reviewer comments, I find that the claim "we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration" is overstated.

The reviewer correctly points out several critical issues:
1. Limited novelty - key components like bag-of-features and multi-head attention are standard techniques
2. No special techniques for handling life-long sequences
3. The method dismisses both microscopic and macroscopic time-series characteristics
4. Core lifelong learning challenges are not adequately addressed

While the evidence shows LURM achieves good experimental results, the framework largely combines existing techniques without introducing truly novel approaches for lifelong modeling. The claim of proposing a "novel framework" overstates the technical innovation.

<score>4</score>

Justification:
- The multi-anchor module is noted to be very similar to standard multi-head attention
- The time aggregation module contains "no special trick" according to reviewer
- No clear technical novelty for handling lifelong sequences is demonstrated
- Core lifelong learning challenges like maintaining old information while learning new patterns are not addressed
- The experimental evaluation focuses on relatively simple tasks that don't fully test long-term modeling capabilities
- While performance improvements are shown, they appear to come from combining known techniques rather than novel architectural innovations


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 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:
After reviewing the evidence, the claim about introducing the Bag of Interests (BoI) model appears well-stated. The evidence thoroughly describes the BoI model's design, implementation, and purpose for summarizing items in behavior sequences. The evidence shows detailed technical specifications (vocabulary size of 10^5, clustering approach) and clear explanations of how BoI aggregates user behavior data at an "interest" granularity, inspired by Bag of Visual Words. The figures, particularly Figure 1(a), explicitly illustrate the BoI architecture with its item embedding and large-scale clustering modules. The claim accurately represents what is demonstrated in the evidence without exaggeration.

<score>1</score>


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

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

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

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

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

The claim to be assessed is:

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:

Strengths:

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

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

Weaknesses:

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

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

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

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

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

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

The evidence to be evaluated is:In order to retain details as many as possible, the size of the 'interest' vocabulary 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:
Based on my review of the evidence, this claim appears to be well-stated (score of 1). The evidence clearly demonstrates that BoI is designed to summarize items in behavior sequences by:

1. Using an item embedding module and large-scale clustering approach similar to Bag of Visual Words
2. Creating an "interest vocabulary" of 10^4-10^5 clusters to categorize similar items
3. Generating sparse high-dimensional BoI features through item aggregation
4. Following a comparable methodology to BoVW for feature extraction and representation

The evidence directly supports each aspect of the claim without exaggeration. The paper thoroughly explains the technical implementation and draws appropriate parallels to the Bag of Visual Words approach it was inspired by.

<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:

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:

Strengths:

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

Weaknesses:

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

Minor:

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

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

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

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

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

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

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

The evidence to be evaluated is:In order to retain details as many as possible, the size of the 'interest' vocabulary 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:
After reviewing the claim and evidence, I find that the claim about introducing BoI to summarize items in behavior sequences similar to Bag of Visual Words is generally well-stated. The evidence thoroughly demonstrates:
- The direct inspiration from BoVW is explicitly acknowledged
- The methodology is clearly explained with detailed mathematical formulations
- The implementation details are provided (vocabulary size of 10^5, clustering approach)
- The similarity to BoVW is evident in both concept and execution (clustering items into "interests" like visual words)
- The limitations and design choices are transparently discussed

The claim accurately reflects what the model does without overstating its capabilities or novelty.

<score>1</score>