User Actions and Timestamp Based Personalized Recommendation for E-Commerce System

In many recommender systems, explicit information is sparse or missing. While there is a great measure of user activities, i.e., the implicit user feedback, which implicitly reflects the preference of the user. In the literature, a number of scholars have proposed algorithms around implicit feedback, such as Bayesian Personalized Ranking(BPR). BPR is designed for implicit feedback ranking, simply it is restricted to one kind of action, e.g., purchase in Amazon or clicks a website. While, in most of the E-commerce system, user actions not only contain purchase but also click, collection and cart. Various forms of the action represent different levels of user preference. At the meanwhile, the timestamps of the actions indicate temporal aspects of the user preference. In this paper, we investigate how to combine the type of activities and the timestamp to get better recommendation results. We propose an Actions and timestamp based Bayesian Personalized Ranking model, At-BPR, for personalized ranking in the E-commerce system. The experimental results prove that, compared with BPR and the extension of BPR methods, At-BPR provides better-ranking performance.