A Time-Based Recommender System Using Implicit Feedback

Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations based on explicit ratings on items from other users. However, in some ecommerce environments such as a mobile environment, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user’s purchase time and the item’s launch time in order to increase recommendation accuracy. Based on this method, we built a recommender system for a mobile e-commerce environment and conducted several experiments. Empirical results show our collaborative filtering-based recommender system to be significantly more accurate than a pure collaborative filtering system. Our approach can be applied to building collaborative filtering-based recommender systems for any ecommerce environment without explicit feedback.