ABNS: Association-based negative sampling for collaborative filtering

Abstract

The purpose of the recommendation systems (RS) is to provide personalized information filtering for users by analyzing their interactions with items. However, the sparsity of data in RS, which means that the number of negative samples is much larger than the number of positive samples, causes the necessity of the negative sampling method. Previous work has shown that an appropriate negative sampling method, rather than simple random negative sampling, can significantly improve the performance of recommendation algorithms. Unfortunately, the existing works have significant shortcomings in explainability and computational complexity. To remedy these shortcomings, we conducted a theoretical analysis of the gradient of the Bayesian Personalized Ranking (BPR) loss function and propose an explainable negative sampling method. Moreover, we conducted experiments on real-world datasets and find that the proposed negative sampling method improves the recommendation performance by an average of 10.23% over uniform negative sampling. Finally, the effectiveness of the proposed method is demonstrated through comparison with other methods and hyperparameter sensitivity analysis.