Cross-domain Recommendation from Implicit Feedback

Jinhao Li, Mengjia Wu, Yi Zhang, Ying Ding, Yizhou Sun, Feng Liu

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Keywords: recommender systems, transfer learning

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Abstract:

Existing cross-domain recommendation (CDR) algorithms aim to leverage explicit feedback from richer source domains to enhance recommendations in a target domain with limited records. However, practical scenarios often involve easily obtainable implicit feedback, such as user clicks, and purchase history, instead of explicit feedback. Thus, in this paper, we consider a more practical problem setting, called cross-domain recommendation from implicit feedback (CDRIF), where both source and target domains are based on implicit feedback. We initially observe that current CDR algorithms struggle to make recommendations when implicit feedback exists in both source and target domains. The primary issue with current CDR algorithms mainly lies in that implicit feedback can only approximately express user preferences in the dataset, inevitably introducing noisy information during the training of recommender systems.
To this end, we propose a noise-aware reweighting framework (NARF) for CDRIF, which effectively alleviates the negative effects brought by the implicit feedback and improves recommendation performance. Extensive experiments conducted on both synthetic and large real-world datasets demonstrate that NARF, implemented by two representative CDR algorithms, significantly outperforms the baseline methods, which further underscores the significance of handling implicit feedback in CDR. The code is available in an anonymous Github repository: https://anonymous.4open.science/r/CDR-3E2A/README.md.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 3221