Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
arxiv(2024)
摘要
In implicit collaborative filtering, hard negative mining techniques are
developed to accelerate and enhance the recommendation model learning. However,
the inadvertent selection of false negatives remains a major concern in hard
negative sampling, as these false negatives can provide incorrect information
and mislead the model learning. To date, only a small number of studies have
been committed to solve the false negative problem, primarily focusing on
designing sophisticated sampling algorithms to filter false negatives. In
contrast, this paper shifts its focus to refining the loss function. We find
that the original Bayesian Personalized Ranking (BPR), initially designed for
uniform negative sampling, is inadequate in adapting to hard sampling
scenarios. Hence, we introduce an enhanced Bayesian Personalized Ranking
objective, named as Hard-BPR, which is specifically crafted for dynamic hard
negative sampling to mitigate the influence of false negatives. This method is
simple yet efficient for real-world deployment. Extensive experiments conducted
on three real-world datasets demonstrate the effectiveness and robustness of
our approach, along with the enhanced ability to distinguish false negatives.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要