Incorporating Personalized Contextual Information in Item-based Collaborative Filtering Recommendation

Abstract

After reviewing the prior work and problem of collaborative filtering recommendation approaches, an approach incorporating personalized contextual information in item-based collaborative filtering is proposed to solve the problem. The approach provides recommendations based on user personalized contextual information besides the typical information on users and items used in most of the current recommendation systems. In this paper, several approaches are proposed to calculate context-based item differences, learn personalized contextual information for every user and predict ratings based on well-known item-based collaborative filtering Slope One. Finally, we experimentally evaluate our approach and compare it to Slope One. The experimental results show that our approach provides more precision recommendations than Slope One.