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[2602.18759] Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity
Summary
This paper presents ICPNS, a novel framework for negative sampling in recommendation systems that utilizes user community structures to enhance the reliability of negative samples derived from implicit feedback.
Why It Matters
Reliable negative sampling is crucial for improving recommendation systems, especially when only positive interactions are available. This research addresses the challenge of designing effective negative sampling strategies by leveraging community structures, which can lead to better user experience and more accurate recommendations.
Key Takeaways
- ICPNS framework utilizes user community structures for negative sampling.
- Improves reliability of negative samples in implicit feedback scenarios.
- Demonstrates consistent performance improvements across various datasets.
- Addresses challenges of realness, hardness, and interpretability in negative sampling.
- Outperforms existing negative sampling strategies in benchmark tests.
Computer Science > Information Retrieval arXiv:2602.18759 (cs) [Submitted on 21 Feb 2026] Title:Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity Authors:Chen Chen, Haobo Lin, Yuanbo Xu View a PDF of the paper titled Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity, by Chen Chen and 1 other authors View PDF HTML (experimental) Abstract:Learning from implicit feedback is a fundamental problem in modern recommender systems, where only positive interactions are observed and explicit negative signals are unavailable. In such settings, negative sampling plays a critical role in model training by constructing negative items that enable effective preference learning and ranking optimization. However, designing reliable negative sampling strategies remains challenging, as they must simultaneously ensure realness, hardness, and interpretability. To this end, we propose \textbf{ICPNS (In-Community Popularity Negative Sampling)}, a novel framework that leverages user community structure to identify reliable and informative negative samples. Our approach is grounded in the insight that item exposure is driven by latent user communities. By identifying these communities and utilizing in-community popularity, ICPNS effectively approximates the probability of item exposure. Consequently, items that are popular within a user's community but remain unclicked are identified as more...
