Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System

摘要

Users & x2019; feedback information as the ground-truth has attracted a lot of attention in recommender systems. However, the feedback that could be contaminated by users & x2019; misoperations or malicious operations is probably not true in real scenarios. This work aims to develop a technique based on an improved Bayesian personalized ranking (BPR), called adversarial training-based mean Bayesian personalized ranking (AT-MBPR). In this method, we divide the feedback information into three categories based on the mean Bayesian personalized ranking (MBPR), then gain the implicit feedback from the mean and non-observed items of each user, following which, adversarial perturbations are added on the embedding vectors of the users and items by playing a minimax game to reduce the noise. The experiments demonstrate in five datasets that our approach outperforms the traditional BPR methods and state-of-the-art methods used for the recommendation. Our implementation is available at: https://github.com/HanXia001/Adversarial-Training-based-Mean-BPR-for-Recommender.