Paper: Multi-interest Disentangled Representation Learning for Multimodal Recommendation (ICLR-2024)

Reviewer comment (Novelty):
The novelty of the proposed model is limited. From my reading, the authors just combined the existing modules together, such as contrastive learning and deep learning.

Author response:
As you can see, our analysis of ablation experiments shows that the various components of the model are valid. This just happens to illustrate the overall effectiveness of our approach. Although the individual modules we use may seem to already exist, our overall design idea is novel. First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. I hope you can reconsider our design ideas.

Reviewer comment (Reproducibility):
I am not sure how the authors implement their proposed model. From the manuscript, the authors seem to derive the objective of their model, but ignoring the detail of implementation. It is hard to reproduce the proposed model.

Author response:
We can publish our implementation details at any time if needed. Due to the limited manuscript pages and the fact that we were rushing the paper, we did not add code to the appendix. But if we're allowed, we can upload our code.

Reviewer comment (Theory):
The method in Section 4.2.3 is confusing. It is not reasonable for modal-specific interest representation and R to maximize mutual information, because S_X in R is modal shared. The authors later additionally minimize mutual information modal specific interest representation and S_X. This raises the question of why to maximize in the first place.

Author response:
We hope that the original feature information can be reconstructed through modal sharing representation and modal specific representation. As for the definition of mutual information loss, we adopt a general approach, similar to the DienCDR model, and our aim is to achieve information reconstruction. Because the greater the mutual information, the higher the correlation between the two variables, that is, the same key information is retained.

Reviewer comment (Experiments):
The present method introduction and experimental results do not show that the method proposed by the author effectively solves the problems proposed in the motivation. The author should use more convincing explanations or design experiments to show this.

Author response:
First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. As you can see, our analysis of ablation experiments shows that the various components of the model are valid.

Reviewer comment (Theory):
How does the proposed approach correspond to the motivation? Is there a strong argument or proof that the method improves the correspondence problem, rather than simply improving the overall performance?

Author response:
First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. As you can see, our analysis of ablation experiments shows that the various components of the model are valid.

Reviewer comment (Evaluation):
The experimental data of w/o-OC and r/p-IR on the baby dataset in the ablation experiment are strange, which I feel may need an appropriate explanation.

Author response:
Since the baby dataset is the sparsest dataset, the training method for it needs to be more effective. Therefore, when we remove OC and IR, which can significantly affect the representation effect, the model effect has a relatively large decline.

Reviewer comment (Novelty):
From a general overview of the paper, it becomes hard to find the real novelty of the proposed approach, as it encompasses several winning approaches from the recent literature.

Author response:
Thank you very much for your careful review and your valuable comments.

In response to your question, we make the following reply:

As you can see, our analysis of ablation experiments shows that the various components of the model are valid. This just happens to illustrate the overall effectiveness of our approach. Although the individual modules we use may seem to already exist, our overall design idea is novel. First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. I hope you can reconsider our design ideas.

Reviewer comment (Novelty):
Even after considering the rebuttal, the reviewer still cannot see the technical novelty provided by the overall MIDR approach, which still appears to be a combination of various winning approaches from the recent literature.

Author response:
Thank you very much for your careful review and your valuable comments.

In response to your question, we make the following reply:

As you can see, our analysis of ablation experiments shows that the various components of the model are valid. This just happens to illustrate the overall effectiveness of our approach. Although the individual modules we use may seem to already exist, our overall design idea is novel. First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. I hope you can reconsider our design ideas.

Reviewer comment (Writing):
The methodology section from the paper is not helpful to clarify the technical novelty aspects. It is very hard to follow the formalism and methodology due to the numerous missing conceptual details in section 4, which are sometimes taken for granted.

Author response:
Thank you very much for your careful review and your valuable comments.

In response to your question, we make the following reply:

As you can see, our analysis of ablation experiments shows that the various components of the model are valid. This just happens to illustrate the overall effectiveness of our approach. Although the individual modules we use may seem to already exist, our overall design idea is novel. First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. I hope you can reconsider our design ideas.

Reviewer comment (Novelty):
With reference to weakness 2, all model’s components are contributing to the overall performance; however, what is the main solution MIDR is proposing to the outlined issues?

Author response:
Thank you very much for your careful review and your valuable comments.

In response to your question, we make the following reply:

As you can see, our analysis of ablation experiments shows that the various components of the model are valid. This just happens to illustrate the overall effectiveness of our approach. Although the individual modules we use may seem to already exist, our overall design idea is novel. First of all, the multi-interest-based theoretical analysis and derivation in Section 4.1 directly established the connection between the user preference factor we considered (interest) and the goal we want to optimize (accurate matching). Based on such a strong theoretical connection, we carried out the design of the following parts. In Section 4.2, we present a multi-interest decoupling representation method based on conditional probability, which leads to the effective learning of modal-sharing and modal-specific interest representation. Then in section 4.3, it appears to be a contrast learning, but it is actually a goal related to the optimized lower bound estimation we defined in Section 4.1, rather than a simple contrast learning. It is precisely through this analysis and derivation that we conclude that the lower bound of loss based on interest prediction can be approximated by the method of comparing the form of learning objectives. Therefore, these are our core ideas and innovation points. I hope you can reconsider our design ideas.

Reviewer comment (Evaluation):
In Section 5.4, why does the model effect decrease when the embedding dimension is 512? Logically, the desired situation should have a smaller improvement than 256.

Author response:
In Section 5.4, when the embedding dimension is further increased to 512, the effect of the model will slightly decline, which is reasonable both in theory and in practice. Although the larger embedding dimension will guarantee a stronger representation capability in theory, the premise is that the model is fully trained and learned, which is difficult to guarantee. If we follow the logic that the model effect will be stronger if we increase the dimension, we only need to increase infinitely and the model effect will always improve, but this is not possible in practice. Limited by the quality of the data and the difficulty of the task, the model does not always become stronger with the increase of the representational dimension. In addition, in some of the comparison baselines mentioned in this paper, they also show similar experimental phenomena for dimension analysis.