Domain Indexing Collaborative Filtering for Recommender System
Recommendation System;Domain Adaptation;Bayesian Deep Learning
Yes
No ethics review needed.
Can you provide more details on the experimental setup, specifically regarding the tuning of baseline models like PMF, CDL, and DANN? Were these baselines optimized with the same rigor as DICF?

The framework assumes that domain indices remain independent of domain-invariant features. How robust is this assumption in practice, especially when there are overlapping characteristics between domains?
The paper uses a domain indexing approach with the adversarial Bayesian. This idea is innovative and enhances the interpretability of "black-box" recommendation system.

The paper addresses the cold-start issue by isolating domain-specific and domain-generalizable features, leading to better recommendations in data-poor domains. This isolation process is efficient and meaningful.
The paper introduces Domain Indexing Collaborative Filtering (DICF), an adversarial Bayesian framework designed to address the cold-start problem in cross-domain recommendation systems. 

DICF infers domain-specific indices that capture domain relationships and enhance the interpretability of cross-domain knowledge transfer. The approach is validated on synthetic (Rec-15 and Rec-30) and real-world (XMRec) datasets, demonstrating superior performance over methods like Domain Adversarial Neural Networks (DANN) and Taxonomy-Structured Domain Adaptation (TSDA). The model is good at producing recommendations with domain-specific and generalizable features.
DICF(introduced by this paper) relies on data's meaningful domain relationships for effective knowledge transfer. This dependence may limit its generalization potential in scenarios with unrelated or highly diverse domains, where domain indices may not capture relevant transfer patterns.

The model is not explicitly designed to handle evolving domains, where domain characteristics and user preferences can change over time. This limits its utility in fast-evolving industries where product turnover and user preferences shift frequently, potentially requiring regular model retraining.
Yes
No ethics review needed.
Overall, the proposed method feels formulaic and lacks a unique innovative aspect. Additionally, the experiments do not include sufficient datasets or baseline methods to fully establish the advancement of the method.
1.	This paper presents a new adversarial Bayesian approach, DICF, which infers domain indices for cross-domain recommendation.
2.	Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of DICF.
3.	The research topic, cross-domain recommendation, is promising and highly relevant to practical applications.
This paper addresses the cold-start challenge in cross-domain recommendation systems by introducing a novel framework, Domain Indexing Collaborative Filtering (DICF). DICF is an adversarial Bayesian approach that infers domain indices during recommendation, enhancing both performance and interpretability in cross-domain knowledge transfer.
1.	The novelty of this paper is limited. The framework appears to be a combination of commonly used techniques, and the reasoning behind the selection of these techniques—such as the use of the Evidence Lower Bound—lacks clarity.
2.	The experiments are limited in scope, as they include a small number of datasets, raising concerns about the generalizability of the proposed method. Additionally, the baselines used are insufficient to verify the method’s state-of-the-art performance.
3.	Figure 3 is difficult to interpret, and it would be beneficial to consider an alternative visualization method to better convey the item context features of Rec-15.
Yes
No ethics review needed.
1. I think there is a mistake in Figure 2. It should be $p(\beta|\gamma)$ instead of $p(\gamma|\beta)$, right? The input should be $\gamma$ if my understanding is right.

2. Results from two synthetic datasets are not convincing because they are generated from the assumption your model has. When synthetic data is generated with assumptions aligned to the model, it can indeed inflate performance results.
1. The method DICF combines Bayesian modeling and adversarial learning, allowing fine-grained feature separation. This ensures that only domain-invariant features contribute to the recommendation, reducing noise and improving prediction accuracy.
2. The probabilistic graphical model and ELBO-based training enhance DICF's ability to capture complex dependencies among features and improve model generalization across different domains.
This paper focuses on cold-start problem in cross-domain scenarios. Two terms are intensively emphasized: domain index and spurious features (domain-specific and cannot generalize across domains). The former aggregates from spurious features and captures relationships among domains. The method is based on two properties of  spurious features. A probabilistic graphical model is used, incorporating domain-specific and invariant features into the recommendation process. The authors conducted experiments on two synthetic datasets and a real-world dataset. It enhances recommendation quality across domains by introducing interpretable domain indices.
1. The introduction is too simple and not clear. Giving a toy example would be better to illustrate the problem of this paper. 
2. This paper does not point out the challenges of problem or motivation of its method.
3. Other weaknesses can be found in Questions section.
Yes
No ethics review needed.
See Weaknesses.
S1: The writing and structure of this paper is clear.

S2: The approach is innovative, employing an adversarial Bayesian method for cross-domain recommendation—a technique that has not been previously explored.
This paper focuses on cross-domain recommendation and proposes an adversarial Bayesian framework called Domain Indexing Collaborative Filtering (DICF). Extensive theoretical analysis and experiments on three datasets demonstrate the effectiveness of the approach. Furthermore, visualizing domain indices intuitively illustrates its effectiveness by showing the correlation between the domain indices and the geographical/continental information of the countries.
W1: As for the chosen baselines, since this paper focuses on cross-domain recommendation, but no baselines specific to this area were selected, instead, only baselines related to domain adaptation are used. More appropriate baselines in cross-domain recommendation are needed.

W2: Regarding the datasets, since numerous public cross-domain recommendation datasets are available, why the authors choose to use a synthetic dataset, which is relatively small. Compared to established datasets like Amazon, Rec-15, and Rec-30, which contain only 750 and 1,500 users and items, this smaller dataset is less convincing.

W3: Furthermore, the related work section should be expanded, particularly concerning cross-domain recommendation. The paper only includes studies prior to 2022, and more recent research outputs should be incorporated.

Overall, although the framework is theoretically solid, technically, it heavily relies on prior work, specifically VDI [1], which gives the impression of being incremental. Furthermore, issues remain with the selection of baselines and datasets.

[1]. Xu, Zihao, et al. "Domain-indexing variational bayes: Interpretable domain index for domain adaptation." arXiv preprint arXiv:2302.02561 (2023).
@inproceedings{
anonymous2024domain,
title={Domain Indexing Collaborative Filtering for Recommender System},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=PnZ2lbQaao},
note={under review}
}
In cross-domain recommendation systems, addressing cold-start items remains a significant challenge. Previous methods typically focus on maximizing performance using cross-domain knowledge, often treating the knowledge transfer process as a black box. However, the recent development of domain indexing introduces a new approach to better address such challenges. We have developed an adversarial Bayesian framework, Domain Indexing Collaborative Filtering (DICF), that infers domain indices during cross-domain recommendation. This framework not only significantly improves the recommendation performance but also provides interpretability for cross-domain knowledge transfer. This is verified by our empirical results on both synthetic and real-world datasets.
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Recommendation System Domain Adaptation Bayesian Deep Learning
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probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Domain Indexing Collaborative Filtering for Recommender System
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