Using temporal user profiles in collaborative filtering recommender system

Abstract

Time-aware recommender systems extend traditional recommendation methods by revealing user preferences over time or observing a specific temporal context. Among other features and advantages, they can be used to provide rating predictions based on changes in recurring time periods. Their underlying assumption is that users are similar if their behavior is similar in the same temporal context. Existing approaches usually consider separate temporal contexts and generated user profiles. In this paper, we create user profiles based on multidimensional temporal contexts and use their combined presentation in a user-based collaborative filtering method. The proposed model provides user preferences at a future point in time that matches temporal profiles. The experimental validation demonstrates that the proposed model is able to outperform the usual collaborative filtering algorithms in prediction accuracy.