Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a method (IEDR) for recommendation systems which explicitly models the intrinsic representations and the extrinsic representations. Intrinsic representations and extrinsic representations are invariant and variant to context features, respectively; a proposed contractive learning component and a mutual information minimization-based disentangling component are collaboratively utilized. Experiments on public datasets demonstrate IEDR’s effectiveness over the state-of-the-art methods.
strength_and_weaknesses: This paper is well written and clearly presented. 

The idea of splitting up context-invariant intrinsic factors and context-based extrinsic factors are interesting and inspiring.

The mutual information minimization-based disentangling component looks over-designed. This component directly introduces the alternative optimization procedure. As such, a natural question is not addressed: does IEDR suffer from more computational overhead? This issue should be carefully discussed and also empirically validated in the experiment section.

The implementation of IEDR in Section 5.1.2 is not sufficiently clear. Is the user behavior sequence not used as a context for IEDR? If so, the experimental results of this section is surprising in some sense.
clarity,_quality,_novelty_and_reproducibility: This paper is well written and clearly presented. 

The code should be made public to validate *reproducibility* owing to the fact that implementation details are missing in the text.

*Quality* and *novelty* is convincing (see Strength and Weaknesses).
summary_of_the_review: See “Strength And Weaknesses” and “Clarity, Quality, Novelty And Reproducibility”.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes an intrinsic-extrinsic disentangled model named IEDR for recommendation. The idea is that user-related (or item-related) features are intrinsic factors that don't change with time, while contextual features are extrinsic factors that change with time.

Thus they aim to get the intrinsic factors by maximizing the agreement between the encoded representation of the same user under different contexts, and minimizing the agreement between the encoded representation of the same context with different users. This is done by optimizing an InfoNCE loss in the contrastive learning module.

Besides, they also try to disentangle extrinsic factors by minimizing the mutual information between the extrinsic factors and the above-generated intrinsic factors.

Experiments are conducted on two public datasets to conclude the superior performance of IEDR in contrast to other CTR prediction models.
strength_and_weaknesses: S1. Overall, the paper is well-organized and well-written. The main idea is clearly illustrated and the figures are quite helpful for understanding the proposed IEDR method.

S2. They conduct extensive experiments on two public datasets, including performance comparison, ablation study, visualization, and case study. The main comparison results are promising and the other results meet their expectations.

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W1. The main concern is about the motivation of the proposed method. The paper claims that intrinsic factors are user-related (or item-related) factors that don't change with context, while extrinsic factors are contextual features that don't change with user or item. 

Then it is straightforward to just use user-related and item-related features as intrinsic factors, and use contextual features as extrinsic factors. It doesn't bring any benefit to getting the intrinsic factors by extracting context-free factors from the input of both contextual features and user (item) features. Thus the method seems to be superfluous as it is trying to seek an inferior answer with the best answer in mind.

I have noticed the clarification of preventing the trivial solution in Appendix C. But I don't think it is a trivial solution to map user features to intrinsic factors and to map contextual features to extrinsic factors, because they are just the right answer according to both the motivation and the proposed method.

W2.  Although the paper conducted experiments on two public datasets, the used datasets are both very small datasets with less than one million samples, which hurts the convincing of the comparison results. It is strongly suggested to add results on benchmark CTR prediction datasets like Avazu or Taobao.
clarity,_quality,_novelty_and_reproducibility: The clarity and quality are good. The idea is novel but not well-motivated.
summary_of_the_review: Due to the weakness in motivation and evaluation, I would vote for rejection.

---
AFTER THE AUTHOR RESPONSE:

The author's response does help to solve my confusion about the motivation of the proposed method. 

I'm still a bit worried about the convincing of the experimental results.  The authors explain that Taobao and Avazu datasets are not suitable for the scenario because they only span less than two weeks. But there are some other available benchmark datasets like Movielens-20M with timestamp features that span decades. My suggestion is that at least the main comparison result should also be provided for the two Amazon datasets in addition to Frappe and Yelp.

Due to the above points, I would like to raise my score to marginally above the acceptance threshold.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a generic model to learn intrinsic and extrinsic factors from various contexts using a contrastive learning component, and a disentangling component. Experimental results on real-world datasets demonstrate the effectiveness of the method. Additionally, theoretical proofs are provided for the refined vCLUB.
strength_and_weaknesses: Strength: 
1.	The paper proposes a good perspective to learn disentangled representations using contrastive learning.
2.	It is interesting to use psychological theory to divide users' purchasing decisions into intrinsic and extrinsic reasons
3.	The rich experiment results prove the effectiveness of the method.

Weakness: 
1.	The proposed method learns two types of disentangled representations. I’m curious about how authors can distinguish whether they are intrinsic or extrinsic factors. 
2.	Some technics are based on previous work. Despite some improvement, it is still not innovative enough, e.g., existing SIGN, vCLUB, using contrastive learning to learn disentangled representations.
3.	About the experiment part, when comparing the sequential recommending methods, there is no detailed description of the settings of the proposed method, like how they process.


clarity,_quality,_novelty_and_reproducibility: The research problem is well-motivated and extensive experiments prove the effectiveness of the proposed method. The paper is innovative, but not sufficient. The authors do not give a detailed description of their experiment setting for reproducibility.
summary_of_the_review: The research problem is well-motivated. The authors give completed theoretical proofs and conduct rich experiments. But the technic is lack innovation.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors discussed intrinsic and extrinsic factors that jointly affect users’ decisions in items selection. This problem is interesting and important. This paper provides a new factor learning method for more contexts that existing studies have not mainly considered.
The contributions are:
C1. Proposing a context-agnostic intrinsic and extrinsic factors learning framework that can tackle different contexts.
C2. Exploiting a context-invariant contrastive learning and a mutual information minimization-based disentangling to disentangle the factors, and providing an explainable results about the different factors.
C3. Providing a theorem analysis about the proposed contrastive loss.
C4. Experiments show the effectiveness of the proposed framework.

strength_and_weaknesses: Strength:
S1. Interesting problem and novel idea in recsys. 
S2. Addressing the issue of factors learning in complex contexts and disentangling the factors into intrinsic and extrinsic parts for better recommendation.
S3. Providing some theorem analysis about the optimization problem.

Weaknesses: 
W1. Some claims are not clear. For example, in 4.2.1, why choosing the negative pairs from the same context with different users? Are there any assumptions? 
W2. The proposed method seems to be complicated. Should give a specific analysis of the time complexity.
W3. Please provide more analysis and experiments on other specific context except for the time context in the section 5.1.2.
W4. There is no comparison with other methods in the section 5.3.2. 
W5. The results on Movies and CDs datasets are missing in Table 1.

clarity,_quality,_novelty_and_reproducibility: Most of the paper is understandable and the paper quality is acceptable. The topic is novel and important. However, the authors do not provide codes and it may lead to difficulties of reproducibility.
summary_of_the_review: This paper tries to solve a novel problem in recommender systems. The motivation and method design are clearly presented. However, one main concern is the flexibility of the method since there are many tasks and hyper-parameters need to be optimized, the tuning process might be very complicated.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Summary: This paper proposes IEDR which learns both intrinsic and extrinsic factors for contextual recommender systems (e.g., context can be weather, devices...). A user/item intrinsic factor is context-invariant for a given user/item, while an extrinsic factor will differ based on different contexts for a given user/item. In order to enforce these model assumptions, the authors propose a contrastive learning objective for intrinsic factors: given a user, maximize the agreement between different contexts while minimize the agreement between different users with the same context. In addition, the authors add a mutual information minimization objective to encourage disentanglement between the intrinsic and extrinsic factors. Experimental results show that IEDR performs favorably over other similarly-structured factorization (some of them only implicitly factorize) models.

Strength: The reviewers all agree that the separation between intrinsic and extrinsic factors makes sense as a way to capture different aspect of user preference/item attributes. The connection to psychological research is an interesting perspective. The paper is mostly well written.  

Weakness: Some of the minor weaknesses (source code, model training time) have been addressed during the author response period. A few unresolved concerns from the reviewers include (I have also read the paper myself in the last week due to the paper being borderline so some of these concerns are also shared/expanded by me below): 
* The separation between intrinsic and extrinsic factors, though conceptually straightforward, remains questionable in execution (reviewer TrQ3, N1Ar and ywxS). The definition of intrinsic factors is mathematically clear and it is understandable how a contrastive loss would be useful here (even though there are still some debatable parts about how exactly the sampling is carried out, mostly from reviewer ywxS; I also personally found Eq 1 unclear: it should either be a function of $c_j$ since $c_j$ appears on the numerator or (more likely) take expectation over $c_j$, in which case this expectation is approximated by sampling a different context. Furthermore, Eq 1 is not exactly InfoNCE given that the numerator is not included in the normalizing factor on the denominator). On the other hand, the definition of the extrinsic factors is rather ambiguous. From definition 1, every function $f$ that does not produce an intrinsic factor (i.e., there exist $c_i$ and $c_j$ such that $f(u, c_i) \neq f(u, c_j)$ would be considered as an extrinsic factor, which is too broad to be useful. A natural question arises: why would you choose such a mutual information minimization objective and not something else? I can see these additional disentanglement help as a form of regularization/prior, but that is not because of the separation between intrinsic and extrinsic factors. It almost feels like the authors are too fixated on the narrative of "intrinsic and extrinsic" even though it doesn't exactly fit the paper perfectly. I would feel more comfortable with the extrinsic factors if the authors can also define it from an invariant perspective similar to intrinsic factors. The current definition of "B is everything that is not A" is not very helpful with defining and justifying the learning objective.  
* Concerns about the experimental setup. Reviewer TrQ3 points out the main results are only reported on two very small datasets (Frappe and Yelp) and even the relative larger datasets (Amazon Movies and CDs) are still comparably small among the overall recommender systems literature. This is something I would also agree with -- in recommender systems, it can often be observed that a method which works well on smaller datasets would not necessarily excel on realistically-sized datasets. 
* Reviewer N1Ar concerns that the technics used in the paper are most from existing work which lacks innovation. I think this is a fair observation but I don't think it as a main weakness (especially comparing with the two above).



justification_for_why_not_higher_score: Overall the reviews on this paper are relatively lukewarm for the lack of a better word. The strengths and weaknesses of the paper are pretty unanimously agreed among the reviewers. I have also read the paper myself in the past week. This is by no means a bad paper. But I think the execution on separating the intrinsic and extrinsic factors, as well as the experimental setup (the first two weaknesses above), are the main reasons that I did not give a higher score.
Decision: Reject