Bayesian Personalized Ranking with the Synthesis of Multiple User and Item Classification

Bayesian Personalized Ranking with the Synthesis of Multiple User and Item Classification

In recent years, rapidly evolving networks have facilitated data interactions between users and goods, and recommender systems have emerged due to the needs of the times. Implicit feedback in recommender systems always is divided into the observed item set and unobserved item set. However, items in the unobserved set often are treated equally, and the items that are of potential interest to users are not sufficiently exploited from unobserved items. In this paper, we come up with a method to solve the problem of the insufficient exploration of unrated items, and we contribute to the additional alleviation of rating sparseness. To do so, we propose a novel Bayesian personalized ranking with the synthesis of multiple users and item classification (BPRS). For each user, we divide the items into three categories, which are positive, interest and negative. We conduct multiple user classification and item classification to exploit the items that are of interest and generate the final ranking. Experiments on three real-world datasets demonstrate the effectiveness of our algorithm for greatly improving the accuracy of recommendation results and alleviating the cold-start problem.