Publication

Abstract : Recommendation systems are being widely adopted in many areas which include social networking, e-commerce etc. Long years of research have led to the proposal of many algorithms in order to effectively capture the real tastes of users and deliver the recommendations accurately. Collaborative filtering is considered to be one of the popular and successful approaches to provide recommendations. In this paper, we conduct a performance evaluation of three popular collaborative filtering algorithms viz. User based, Item based and Slope-one recommender. We illustrate a brief overview on the different approaches of collaborative filtering, their method of working, advantages and limitations. We demonstrate the results based on the evaluation metrics precision, recall, f-measure, fallout and reach. Our experiments revealed that the Slope-one approach outperformed the other two approaches based on the evaluation metrics. We also explored different kinds of similarity metrics and highlighted the effect of size of the neighbourhood on the evaluation metrics. Keywords: Collaborative Filtering (CF), Recommendation systems, Apache Mahout, User based CF, Item based CF, Slope one.