Feature enhanced time-aware recommender system

Traditional recommender systems utilize user and item profiles in order to predict ratings of unseen items. New users, items and ratings are continuously updated to the system, making data available for detection of changes in user preferences throughout the time. In this work the widely used user-neighborhood recommender system is extended by incorporating temporal information and enhancing measure of neighborhood similarity with information on item features. Unlike other models, we also add time-weight function in the preference prediction step to improve prediction accuracy. Experiments on real data set show an improvement in prediction performance over traditional collaborative filtering model.