Denoising Implicit Feedback for Cold-start Recommendation

Computer Science > Artificial Intelligence [Submitted on 17 Jun 2026] Denoising Implicit Feedback for Cold-start Recommendation Gaode Chen, Shicheng Wang, Shikun Li, Rui Huang, Xinghua Zhang, Yunze Luo, Shipeng Li, Shiming Ge, Ruina Sun, Yinjie Jiang, Jun Zhang Implicit feedback is widely used in recommender systems due to its accessibility and generality, yet it usually presents noisy samples (e.g., clickbait, position bias). Meanwhile, recommenders inevitably face the item cold-start problem due to the continuous influx of new items. We identify that cold items are more prone to noisy samples due to the aforementioned factors, and researchers often overlook the significance of denoising implicit feedback for cold items. Previous denoising studies usually identify noisy samples based on heuristic patterns, such as higher loss values, and mitigate noise through sample selection or re-weighting. However, these methods have limited adaptability and are ineffective in cold-start scenarios. To achieve denoising implicit feedback for cold-start recommendation, we propose a model-agnostic denoising method called DIF. First, user preferences for content remain stable, which allows us to infer pseudo-labels indicating whether a user is interested in a cold item through content-similar warm items. Furthermore, to improve pseudo-label accuracy, we model the confidence of pseudo-labels based on the content similarity between the cold item and warm items, and then aggregate multiple pseudo-labels for each sample. Finally, we explicitly estimate the uncertainty of the noisy sample label by considering its relative entropy and the cold-start status of the item, which adaptively guides the role of pseudo-labels to correct the noisy labels at the sample level. DIF's superiority is supported by both theoretical justification and extensive experiments on real-world datasets. The method has been deployed on a billion-user scale short video application Kuaishou and has significantly improved various commercial metrics within cold-start scenarios. Comments: Accepted by KDD 2026 ADS Track Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Multimedia (cs.MM) Cite as: arXiv:2606.19658 [cs.AI]   (or arXiv:2606.19658v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.19658 Focus to learn more Submission history From: Shikun Li [view email] [v1] Wed, 17 Jun 2026 23:50:45 UTC (229 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.IR cs.MM References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)