Jingtao Ding

Sampler Design for Bayesian Personalized Ranking by Leveraging View Data

2019 · IEEE Transactions on Knowledge and Data Engineering

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 …

Reinforced Negative Sampling for Recommendation with Exposure Data

2019

In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we …