A Decision Tree Based Context-Aware Recommender System

Abstract

Context-aware recommender systems (CARSs) have emerged from traditional recommender systems (RSs) that provide several different opportunities in the area of personalized recommendations for online users. CARSs promote incorporation of additional contextual information such as time, day, season, user’s personality along with users and items related information into recommendation process that makes market based e-commerce sites more attractive to users. Content-based filtering (CBF) and collaborative filtering (CF) are two well-known and most implemented recommendation techniques that offer various hybridization approaches for producing quality recommendations. Moreover, contextual pre-filtering, contextual post-filtering and contextual modeling are some paradigms through which CARSs take advantages of user’s contextual preferences in recommendation process. In this paper, we introduce a decision tree based CARS framework that exploits the benefits of both CBF and CF techniques using contextual pre-filtering paradigm. We apply ID3 algorithm for learning a user model to exploit the user’s contextual preferences and utilizing rules extracted from decision tree to neighborhood formation. Experimental results using two real-world benchmark datasets clearly validate the effectiveness of our proposed scheme in comparison to traditional scheme.