An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data.
WWW '18: The Web Conference 2018 Lyon France April, 2018(2018)
摘要
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of the negative sampler. In this short paper, we make two contributions with respect to BPR. First, we find that sampling negative items from the whole space is unnecessary and may even degrade the performance. Second, focusing on the purchase feedback of the E-commerce domain, we propose a simple yet effective sampler for BPR by leveraging the additional view data. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-aware sampler enhances BPR with a relative improvement of 27.36% and 69.54% on two real-world datasets respectively.
更多查看译文
关键词
BPR, recommendation, sampler, view data
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络