Collaborative Filtering Approach based on Item and Personalized Contextual Information

Abstract—In order to improve the precision of rating prediction for personalized recommendation online, an approach incorporating personalized contextual information in item-based collaborative filtering is proposed. In this paper we analyze how to learn personalized contextual information and predict ratings for unknown items based on the well-known SlopeOne item-based collaborative filtering. Finally, we experimentally evaluate our results and compare them to the basic Slope One approach. Our experiments suggest that our algorithm provide better quality than Slope One algorithm