Using Temporal Information in Collaborative Filtering: An Empirical Study

- Collaborative filtering is a widely used and proven method of building recommender systems that provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an important factor in some e-commerce environments (such as a mobile environment as a result of limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy. In this paper, we present a variety of temporal information and investigate how such temporal information affects the accuracy of collaborative filtering-based recommender systems. The temporal information includes item launch time, user buying time, the time difference between the two, as well as several combinations of these three types of temporal information. We conducted several experiments on a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results ...

• Type: Conference
• Year: 2006
• Where: CSREAEEE
• Authors: Young Park, Tong-Queue Lee
• Keywords: Collaborative Filtering, CSREAEEE 2006, CSREAEEE 2007, E-commerce Environments, Temporal Information