Time-Dependent Models in Collaborative Filtering Based Recommender System

In recent years, time information is more and more important in collaborative filtering (CF) based recommender system because many systems have collected rating data for a long time, and time effects in user preference is stronger. In this paper, we focus on modeling time effects in CF and analyze how temporal features influence CF. There are four main types of time effects in CF: (1) time bias, the interest of whole society changes with time; (2) user bias shifting, a user may change his/her rating habit over time; (3) item bias shifting, the popularity of items changes with time; (4) user preference shifting, a user may change his/her attitude to some types of items. In this work, these four time effects are used by factorized model, which is called TimeSVD. Moreover, many other time effects are used by simple methods. Our time-dependent models are tested on Netflix data from Nov. 1999 to Dec. 2005. Experimental results show that prediction accuracy in CF can be improved significantly by using time information.