2018/09/21 by Jingtao Ding, Ding, Jingtao, Guanghui Yu +7 · 11 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Bandit Algorithms Research #Artificial intelligence #Bayesian inference #Bayesian probability #Business process reengineering #Computer science #Data Stream Mining Techniques #Data mining #Engineering #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Information retrieval #Machine learning #Mathematics #Pairwise comparison #Preference #Process (computing) #Quality (philosophy) #Ranking (information retrieval) #Recommender Systems and Techniques #Sampling (signal processing) #Statistics #Weighting #cs.IR
paper · pdf · doi:10.48550/arxiv.1809.08162
published in arXiv (Cornell University) (Cornell University) · submitted to IEEE Transactions on Knowledge and Data Engineering
arxiv created 2018/09/21 · openalex publication_date 2018/09/21 · arxiv updated 2018/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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 negative sampler. In this 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 E-commerce, we propose an effective sampler for BPR by leveraging the additional view data. In our proposed sampler, users' viewed interactions are considered as an intermediate feedback between those purchased and unobserved interactions. The pairwise rankings of user preference among these three types of interactions are jointly learned, and a user-oriented weighting strategy is considered during learning process, which is more effective and flexible. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-enhanced sampler enhances BPR with a relative improvement over 37.03% and 16.40% on two real-world datasets. Our study demonstrates the importance of considering users' additional feedback when modeling their preference on different items, which avoids sampling negative items indiscriminately and inefficiently.