Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 9: Top 15% of accepted NeurIPS papers, strong accept
review_content: summary: This paper focuses on solving the cold-start issue in Cross-Domain Recommendation (CDR) setting. For this purpose, the authors propose a DisAlign framework, which mainly has two modules, i.e., rating prediction module and embedding distribution alignment module. The main novelty relies in the embedding distribution alignment module. In it, the authors propose Stein path alignment and proxy Stein path alignment. The experiments on benchmark datasets, compared with six baselines, show the superior of the proposed model. The experimental analysis also shows the effectiveness of the model ability. 
main_review: - Originality: 
Data sparsity is a long-standing problem in recommender systems. Solving this problem in CDR setting by proposing distribution alignment is novel to me. The proposed Stein path alignment and proxy Stein path alignment are novel to me, and the accuracy improvement brought by the proposed models is quite promising.

- Quality: 
  This work is technically written with detailed algorithms. Most of the arguments presented in this work are supported by experiment results. 

- Clarity: 
  This paper is well-organized, and it clearly states the research problem in the introduction. The proposed model is well-motivated and clearly presented. 

- Significance: 
  To my knowledge, this paper is the first work solving the cold-start problem in CDR setting. The experiments show remarkable improvements (in terms of HR, Recall, and NDCG) over the prior models.  

limitations_and_societal_impact: 1. This paper focuses on the setting where all the items are cold in the target domain, but most recommender systems consider some items as warm items and the remaining as cold items. Is this setting also suitable for the proposed method?
2. What specific data pre-processing methods are used during experiments?
3. Why DisAlign-SP outperforms DisAlign-PSP in some cases?
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: Summary:

The paper addresses the cold-start item recommendation with auxiliary information. To attack the heterogeneity/ domain discrepancy across the source domain and target domain, Stein path is adopted to align the domain distributions. In specific, the distribution of target item auxiliary embedding is to be aligned with both source item auxiliary embedding and source item collaborative embedding. A proxy Stein path is proposed to reduce the cubic time complexity. Good experiments are achieved on real-world datasets by comparing with many SOTA methods.

main_review: Strong points:

S1: Achieving good experimental results, outperforming many SOTA methods.


Weak points:

W1: The technical contribution is limited. It might be novel in applying Stein path to the cold-start item recommendation with auxiliary information.

W2: The motivation is not strong. What is the physical meaning of feeding cold item auxiliary embedding   C with warm item collaborative preference V and auxiliary embedding W to the Stein path?  


Other comments:

C1: The examples in Figure 1 and Figure 2 show that the auxiliary information seems to be Visual while it is Text in evaluation experiments.

C2: In terms of Fig 1 and Fig 3, the source domain is Movie while the target domain is Book, while it is not consistent with the body text in Line 138. 

C3: in Fig 2, the L_BM should correspond to the L2 Constraint while the L_BK to Item Clustering. 

limitations_and_societal_impact: n/a
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: In this paper, the authors study a relatively new problem called cross-domain cold-start recommendation (CDCSR), where the goal is to transfer knowledge from a warm source domain with user-item ratings and item descriptions to a cold target domain with item descriptions only (i.e., without any user-item interactions). Notice that the users in two domains are aligned. In particular, the authors propose a novel recommendation framework, i.e., DisAlign, which contains two main modules, i.e., a rating prediction module and an embedding distribution alignment module. 
main_review: 1 Originality: The studied problem, i.e., cross-domain cold-start recommendation (CDCSR), is interesting and challenging. The proposed method, i.e., DisAlign, is new since no previous work addresses the CDCSR problem by aligning the latent distributions using Stein path alignment or its improvement version proxy Stein path.

2 Quality: The proposed model is technically sound, which is also supported by the empirical studies.

3 Clarity: Overall, the paper is well presented and is easy to follow. The problem is clearly defined and the techniques are presented with sufficient details (some are in the appendix). Moreover, some illustraitons are included for better understanding, e.g., Figure 1 for the studied problem, Figure 2 fort the motivation of distribution alignment and Figure 3 of the proxy Stein path alignment.

4 Significance: 
The authors have conducted extensive empirical studies, including 8 cross-domain settings and 6 baseline methods, as well as ablation studies and case studies.


limitations_and_societal_impact: Yes
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: This paper proposes a framework for cross-domain item cold-start recommendation. The main idea follows the algorithm of Stein Variational Gradient Descent, by iteratively transporting the auxiliary embeddings of cold items in the target domain to match the embedding distribution of warm items in the source domain. The authors further propose to reduce computational time costs by selecting typical proxies of cold items.
main_review: Strength
1. This paper is well-organized and mostly clearly written.
2. It is technically sound to apply SVGD to transport the auxiliary embeddings of cold items to match the embedding distribution of warm items. 
3. It is novel to reduce the time cost of SVGD by finding typical proxy samples in the target domain.
4. The experimental results are promising compared with existing methods.
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Weakness
1. It is not clear to me why the auxiliary features of items in different domains could be encoded with a shared network. For example, an item in the movie domain may have three feature fields: director, actor, category while an item in the book domain may have two different feature fields: author, book name. In this way, it doesn't make sense to use a shared network to encode their features. This may limit the applicability of the proposed method in real-world cross-domain recommendation systems. Besides, the so-called two-stream siamese network is not defined, which should be better demonstrated.
2. In the Proxy Stein path alignment section, it is not shown how the embeddings of cold items are updated. Although I am able to understand the general procedure after reading the supplemental materials, it is still suggested to make it clearer in the paper.
3. It is not shown why Equation (2) has to be solved through alternating optimization. There is no proof of the convergence of the alternating optimization procedure. Also, it is not reasonable to say updating F every 15 iterations could guarantee stability.
4. Similar to 3, there is no proof of the convergence of the alternating optimization of multiple proxies in Equation (6).
5. Although the proposed proxy approach can reduce the time cost of the original SVGD, the time complexity is still $O(N^3)$, since $H^*=[\frac{N}{2}]$. Thus the proposed method may still hardly scale to industrial scenarios. Besides, it would be helpful to see how would the size of proxies affect the model performance.
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AFTER REBUTTAL: The authors' responses addressed most of my concerns, and there are still some improvements to be made in the paper for better clarification. Thus I would like to increase my rating to above the threshold.
limitations_and_societal_impact: Yes.
review_title: null

Meta-review: Reviews are quite mixed, and admittedly the one most positive review is somewhat of an outlier, though there's enough here to recommend acceptance. Still somewhat borderline given the two marginal scores (2x5), though with two strong voices for acceptance I'd lean positive.

In spite of the mixed scores, and the detailed rebuttal, opinions didn't really move during the discussion phase. So mostly my judgement is based on the initial scores, as well as what appears to be a reasonable rebuttal (even if it didn't move the needle on scores).

Reviewers were not totally aligned in their opinions. Some praised the originality and significance of the paper (especially the most positive review), while others found the technical contribution more limited. Other issues centered around lack of clarity w.r.t. various components. Ultimately the negative points seemed not to be deal-breakers and could be fixed easily.

Again, enough here to recommend acceptance, and nothing that seems like a red flag, though still somewhat borderline in terms of scores / distribution of scores.
Decision: Accept (Poster)