Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance

Abstract

The main purpose of any recommendation system is to recommend items of users’ interest. Mostly content and collaborative filtering are widely used recommendation systems. Matrix factorization technique is also used by many recommendation systems. All these techniquesproduceconsiderably bigger recommendation list, althoughusers generallyprefer to see fewer recommendations. It means users are interested in smaller recommendations list having items of their interest. To realize this objective, the proposed approach generates smaller top-n item recommendations list by placing users’ unseen items in recommendation listand thus attaining high precision value. The proposed approach uses content based filtering and collaborative filtering collectively. The proposed recommendation system uniquely finds popularity of all items among users in the form of weights. It also uses the rating variance of different items to generate more effective recommendations. The experimental results shows that proposed recommendation system has better precision, even for smaller number of recommendations when compared with other benchmark recommendation methods.