False Negative Sample Aware Negative Sampling for Recommendation

Abstract

Negative sampling plays a key role in implicit feedback collaborative filtering. It draws high-quality negative samples from a large number of uninteracted samples. Existing methods primarily focus on hard negative samples, while overlooking the issue of sampling bias introduced by false negative samples. We first experimentally show the adverse effect of false negative samples in hard negative sampling strategies. To mitigate this adverse effect, we propose a method that dynamically identifies and eliminates false negative samples based on dynamic negative sampling (EDNS). Our method integrates a global identification module and a positives-context identification module. The former performs clustering on embeddings of all users and items and deletes uninteracted items that are in the same cluster as the corresponding user as false negative samples. The latter constructs a similarity measure for uninteracted items based on the positive sample set of the user and removes the top-k items ranked by the measure as false negative samples. Finally, we utilize the dynamic negative sampling strategy to build a sample pool from the corrected uninteracted sample set, effectively mitigating the risk of introducing false negative samples Experiments on three real-world datasets show that our approach significantly outperforms state-of-the-art negative sampling baselines.