Paper: Unifying User Preferences and Critic Opinions: A Multi-View Cross-Domain Item-sharing Recommender System (ICLR-2024)

Reviewer comment (Theory):
There are many incorrect formula details in the article. For example, the authors obtain covariance $\Sigma$ via $W_T’h_T + b_T’ = diag(\Sigma_t)$. However, $\Sigma$ should be positive values. How to guarantee the output of $W_T’h_T + b_T’$ could be always positive?

Author response:
Weakness 1. The confusion regarding $diag{\Sigma_{i}}$ is regrettable. In our code, $W_{T}'h_{T}+b_{T}'$ outputs the log variance, which we then convert to positive variance values using the 'torch.exp' function. This is a very frequently used trick in VAE style code. Since this is a common code trick, we did not mention it in the paper. We will add more explanations in the Appendix for revision.

Reviewer comment (Reproducibility):
Some model details are missing. For example, the authors adopt $g(\cdot)$ as the activation function in Eq.(1). However, what kind of activation function do you use in the experiments? ReLU or Sigmoid? Moreover, the authors adopt the dropout layer in the Eq.(1). How about the dropout ratio for this layer?

Author response:
Weakness 2. The activation function in Eq.(1) is tanh in our experiments. We will add more details about it for revision. 
As introduced in Section 4.1, we set the dropout ratio as 0.5 for the user-rating embedding network.

Reviewer comment (Theory):
Some formulas are completely wrong. For example, the authors obtain user distribution as $p(u_i|R_i) \sim N(\mu_i, \Sigma_i)$. However, the reparameterization process in the paper is $u_i = \mu_i+\epsilon \Sigma_i$. It is completely wrong. The correct answer is $u_i = \mu_i+\epsilon \sqrt{\Sigma_i}$.

Author response:
Weakness 3. We appreciate your correction on the reparameterization formula. This will be amended in the revised paper.

Reviewer comment (Writing):
The methodology is hard to follow with too many notations. I strongly encourage the authors to provide the pseudo algorithm table.

Author response:
Weakness 4. Thanks for your suggestion. We will incorporate a pseudo algorithm table in the revised paper.

Reviewer comment (Experiments):
Some important baselines are missing, e.g., PTUPCDR.

Author response:
Weakness 5. PTUPCDR assumes that there is overlapping users and no shared items, which is opposite to our focused scenario, i.e., item-sharing and no user overlaps. Therefore, PTUPCDR cannot be directly applied in our problem settings and not included in our baseline methods.

Reviewer comment (Writing):
The authors emphasize that critic reviews are much more valuable than common reviews. However, how to define whether the reviews are critic or not?

Author response:
Weakness 6. As mentioned, the distinction between critic and user reviews is inherent in our dataset with labels, negating the need for manual detection.

Reviewer comment (Writing):
hard to follow the methodology

Author response:
We apologize for any prior ambiguities regarding the components of the MCIR framework. 
The MCIR framework is composed of four integral components, each contributing significantly to the methodology:

* User Embedding Networks: This includes two sub-components - the user-rating embedding network and the user-comment embedding network. Their primary function is to learn latent user embeddings from both rating and comment perspectives, respectively.

* Critic Embedding Networks: These networks are designed to concurrently learn latent critic embeddings from both rating and comment perspectives. This dual approach ensures a comprehensive representation of critic opinions.

* Attentive Integrated Mechanism: This mechanism focuses on learning latent item embeddings. It plays a crucial role in transferring auxiliary critic text information into the user domain by enabling shared item embeddings.

* Cross-View Unified Learning with Contrastive Learning: This component is essential for leveraging neighborhood information across user and critic domains. The incorporation of contrastive learning here enhances the effectiveness of using cross-domain neighborhood information to refine the latent user and item embeddings.

The attentive integrated mechanism and cross-view unified learning collectively ensure a robust transfer and utilization of critic information from both the text and graph views.
We have provided a comprehensive ablation study in Section 4.3 and the Appendix, which demonstrates the importance of each component.
In response to your concerns, we will clarify the main components of the MCIR framework and add more intuitive and descriptive explanations when introducing the modules' details in the revised version of our paper.

Reviewer comment (Writing):
unclear definitions

Author response:
We apologize for any prior ambiguities regarding the components of the MCIR framework. 
The MCIR framework is composed of four integral components, each contributing significantly to the methodology:

* User Embedding Networks: This includes two sub-components - the user-rating embedding network and the user-comment embedding network. Their primary function is to learn latent user embeddings from both rating and comment perspectives, respectively.

* Critic Embedding Networks: These networks are designed to concurrently learn latent critic embeddings from both rating and comment perspectives. This dual approach ensures a comprehensive representation of critic opinions.

* Attentive Integrated Mechanism: This mechanism focuses on learning latent item embeddings. It plays a crucial role in transferring auxiliary critic text information into the user domain by enabling shared item embeddings.

* Cross-View Unified Learning with Contrastive Learning: This component is essential for leveraging neighborhood information across user and critic domains. The incorporation of contrastive learning here enhances the effectiveness of using cross-domain neighborhood information to refine the latent user and item embeddings.

The attentive integrated mechanism and cross-view unified learning collectively ensure a robust transfer and utilization of critic information from both the text and graph views.
We have provided a comprehensive ablation study in Section 4.3 and the Appendix, which demonstrates the importance of each component.
In response to your concerns, we will clarify the main components of the MCIR framework and add more intuitive and descriptive explanations when introducing the modules' details in the revised version of our paper.

Reviewer comment (Presentation):
It's hard to follow the methodology sections. Figure 2 doesn't help make it clearer. Instead it makes it more complicated to understand without knowing the meaning of letters and captions. I hope reading through text would help my understanding. However I still don't know why many steps are necessary and why so much components and techniques are required. For example, for definitions, what user-rating network, user-comment network, and critic embedding network. It seems that the final goal is to obtain user and item embeddings aligned in the same latent space. But by nature critic comment is hard to define and detect (please refer to the following question). It seems that contrastive loss and GCN are also included, but by checking Figure 2 are the left and right boxes decoupled? Do they have relationship? And what's their relationship?

Author response:
We apologize for any prior ambiguities regarding the components of the MCIR framework. 
The MCIR framework is composed of four integral components, each contributing significantly to the methodology:

* User Embedding Networks: This includes two sub-components - the user-rating embedding network and the user-comment embedding network. Their primary function is to learn latent user embeddings from both rating and comment perspectives, respectively.

* Critic Embedding Networks: These networks are designed to concurrently learn latent critic embeddings from both rating and comment perspectives. This dual approach ensures a comprehensive representation of critic opinions.

* Attentive Integrated Mechanism: This mechanism focuses on learning latent item embeddings. It plays a crucial role in transferring auxiliary critic text information into the user domain by enabling shared item embeddings.

* Cross-View Unified Learning with Contrastive Learning: This component is essential for leveraging neighborhood information across user and critic domains. The incorporation of contrastive learning here enhances the effectiveness of using cross-domain neighborhood information to refine the latent user and item embeddings.

The attentive integrated mechanism and cross-view unified learning collectively ensure a robust transfer and utilization of critic information from both the text and graph views.
We have provided a comprehensive ablation study in Section 4.3 and the Appendix, which demonstrates the importance of each component.
In response to your concerns, we will clarify the main components of the MCIR framework and add more intuitive and descriptive explanations when introducing the modules' details in the revised version of our paper.

Regarding Figure 2, the left and right boxes are interconnected via the final latent embeddings $p_i$ and $q_j$, which are trained through both the VAE loss in Equation 9  and the contrastive learning loss in Equation 13.
We will try to modify the Figure 2 for better illustration.

Reviewer comment (Reproducibility):
“Experts Write More Complex and Detached Reviews, While Amateurs More Accessible and Emotional Ones” How to know which review is written by an expert or an amateur? Or how to quantify the critic metric for a comment. The statement is also related to Section "Critic Embedding Network" where it mentioned critic rating prediction task. It seems to be based on the inner product of v_j and w_l^c. What does the latent critic-rating vector w_j^c come from?

Author response:
Sorry for the confusion on data. First we want to clarify that, as detailed in Sections 1 and 4.1, and the Appendix, critic and user reviews in our dataset, sourced from Metacritic, are distinctly labeled, eliminating the need to differentiate between expert and amateur reviews. This dataset will be made publicly available upon paper acceptance.

As outlined in Section 3.3, the latent critic-rating vector $w_l^c$ is derived from the latent critic vector $u_l^c$ using a single-layer MLP network ($w_l^c = f_w(u_l^c)$). The vector $u_l^c$ is initially randomly initialized and then refined through iterative learning.

Reviewer comment (Writing):
First of all, the authors did not do a good job of clearly formulating the problem they want to solve. I felt confused after I went over the paper for the first two times. When I first read it, I thought they were trying to do cross-domain recommendations when different domains shared items but not users, which is counter-intuitive. Then I realized it's critic that is shared by different domains. I recommend the authors state this very explicitly and use some space to formulate the problem using some formulas. Besides, the phrase "item-sharing" in the title is quite misleading.

Author response:
First, we are sorry for any ambiguity regarding the term 'cross-domain' in our context. Our work indeed focuses on cross-domain recommendations where different domains share items but not users or critics. Critics are not shared by different domains. 

Traditionally, cross-domain recommendations have been usually defined in the way that users are sharing across different domains in the literature. However, as evidenced by the survey paper [1] and [2] (please see the Figure 1 and Table 1 in [1]), it's essential to recognize that recommendations integrating distinct user groups with full item overlap also fall under the cross-domain recommendation umbrella [1][2][3]. As stated in the paper, the cross-domain in this paper means we leveraging auxiliary information from critic domain data for making better predictions in the user domain. Here 'domain' is defined on the item-level relevance, i.e., there
are common items and different users between the targeted and auxiliary domains, as illustrated in the survey paper [2]. The auxiliary domain is critic domain and the targeted domain is user domain in this paper. We concur that this specific area remains relatively untapped compared to traditional user-sharing cross-domain recommendation, thus underscoring the significance of our contribution. We will add more explanation in the paper for revision.

Reviewer comment (Experiments):
In experiments, did the authors use data from two datasets for training and then predict the rating for the 3rd dataset? How does the evaluation of "Cross-domain" recommendation work in this paper? I did not quite understand after reading Section 4.1.

Author response:
Yes, we independently train and test the model on each dataset. For every dataset, critic-item reviews (ratings and texts) serve as auxiliary domain data, while user-item reviews (ratings and texts) form the targeted domain data. As introduced in Section 4.1, we used 60\% of observed items per user for training, 10\% for validation, and the remaining for testing in the targeted domain, while all auxiliary domain data were used for training. We evaluate our model on the test set in the targeted domain. Many thanks for your question, and we will include more detailed descriptions of our experimental setup in the revised paper.

Reviewer comment (Presentation):
Shouldn't the $y_{lj}$ in the left up corner of Figure 2 be $y^c_{lj}$?

Author response:
Question 1. Thank you. $ y_{lj} $ should be $ y_{lj}^c $ in the left up corner of Figure 2. We will correct this in the revised paper.

Reviewer comment (Theory):
In the user embedding network, why is $R_i$ used to construct $u_i$ instead of the other way around, using $u_i$ to generate $R_i$? The authors may have their rationale for designing the model in this way. But they failed to explain it clearly to the readers.

Author response:
Question 2. As stated in the paper, the user embedding network belongs to the encoder part of the VAE architecture. Hence we use $R_i $ to construct $ u_i $ but not using $ u_i $ to generate $R_i $. The latter is suitable for the decoder part of VAE. We will add more explanation in the revised paper.

Reviewer comment (Theory):
The explanation of the "attentive integrated mechanism" seems to be over-complicated to me. It actually follows the standard design of the attention mechanism with $v_j$ as the query vector and $w^c_l$ as the key and value vector. The introduction of $L_j$ and $w^c_0 = v_j$ does not seem to be necessary and only added complexity.

Author response:
Question 3. Sorry for the confusion around the introduction of $L_j$ and $w_{0}^c=v_j$ in Equation 6. These elements are meant to simplify the mathematical expression. Without the help of $ L_j $ and $ w_{0}^c=v_j $, Equation 6 will look much more complex. According to your comment,  we will refine our explanation of the attentive integration mechanism, particularly focusing on the roles of key, query, and value vectors.

Reviewer comment (Writing):
In the same section, the first sentence says "Given that only the items are shared between the critic and user domains". I believe the word "domain" does not mean the cross-domain studied in this paper is trying to cross the "user" and "critic" domains. Am I right?

Author response:
Question 4.  As previously mentioned, our study defines 'cross-domain' as leveraging critic domain data to enhance user domain predictions. Critics, often from professional media institutions, and users, typically ordinary web contributors, represent distinct groups without overlap. Our study bridges these 'user' and 'critic' domains in a novel cross-domain approach.

Reviewer comment (Experiments):
How useful is the attentive module? For instance, what happens if all \( v^a_j = 0 \)?

Author response:
Weakness 1: The attentive integration mechanism is essential in combining item-summary information with critics' commentary as it adaptively aggregates information, reflecting the varying influences of item summaries and critics’ comments on item characteristics. We have included two case studies in Appendix Section D, demonstrating its effectiveness. In response to your suggestion, we introduce a new variant, MCIR-C6, employing simple average of item-summary vector $v_j^s$ and critic vectors $w_{l}^c$ instead of attentive integration. The comparative results on MC-Game are as follows:

Methods R@5 P@5 MAP@5 NDCG@5

MCIR-C6 0.1050 0.0845 0.0545 0.1172

These results clearly show the superior performance of MCIR over MCIR-C6, affirming the value of the attentive integration mechanism. Regarding the query “what if all $v_j^a=0$?”, in practice, $v_j^a$ will not be zero since the item-summary vector is always present. The variant MCIR-C2, which replaces $v_j^a$ with the item-summary vector $v_j$, underperforms compared to MCIR as shown in Figure 3(a). This further highlights the importance of the review text information alongside the attentive integration mechanism. We plan to include MCIR-C6 in our revised ablation study and provide an in-depth discussion of the attentive integration mechanism.

Reviewer comment (Experiments):
How useful is the graph network when \( \eta_3 = \eta_4 = 0 \)?

Author response:
Weakness 2: Actually, the variant MCIR-C4 is set as $\eta_3=\eta_4=0$. Note that the graph embeddings can only influence the final latent user and item representation $p_i $ and $q_j $ by the contrastive learning loss  $ \mathcal{L}_{CL} $. 

So when we set $\eta_4=0$, the GCN loss $\mathcal{L}_{Graph}$ is irrelevant to the predictions, which can also be viewed as $\eta_3=0$. We will elaborate on this in the revised manuscript.

Reviewer comment (Experiments):
It is difficult to assess the impact of the proposed method (in terms of scope, generalizability, etc.) without understanding the value of its components.

Author response:
Weakness 1: The attentive integration mechanism is essential in combining item-summary information with critics' commentary as it adaptively aggregates information, reflecting the varying influences of item summaries and critics’ comments on item characteristics. We have included two case studies in Appendix Section D, demonstrating its effectiveness. In response to your suggestion, we introduce a new variant, MCIR-C6, employing simple average of item-summary vector $v_j^s$ and critic vectors $w_{l}^c$ instead of attentive integration. The comparative results on MC-Game are as follows:

Methods R@5 P@5 MAP@5 NDCG@5

MCIR-C6 0.1050 0.0845 0.0545 0.1172

These results clearly show the superior performance of MCIR over MCIR-C6, affirming the value of the attentive integration mechanism. Regarding the query “what if all $v_j^a=0$?”, in practice, $v_j^a$ will not be zero since the item-summary vector is always present. The variant MCIR-C2, which replaces $v_j^a$ with the item-summary vector $v_j$, underperforms compared to MCIR as shown in Figure 3(a). This further highlights the importance of the review text information alongside the attentive integration mechanism. We plan to include MCIR-C6 in our revised ablation study and provide an in-depth discussion of the attentive integration mechanism.