IMPROVING RECOMMENDATION QUALITY WITH ENHANCED CORRELATION SIMILARITY IN MODIFIED WEIGHTED SUM

Abstract

Recommender systems aim to help users in finding the items of their interests from large data collections with little effort. Those systems use various recommendation approaches to provide accurate recommendation more and more. Among them, collaborative filtering approach is the most widely used approach in recommender systems. In the two types of CF system, item-based CF systems overtake the traditional user-based CF systems since it can overcome the scalability problem of the user-based CF. Item-based CF system computes the prediction of the user tastes on new items based on the item similarity result from the explicit rating of the users. They predict rating on the new items based on the historical ratings of the users. The proposed system improved the item-based collaborative filtering approach by enhancing the similarity of rating on items with demographic similarity of the items. It modifies one of the prediction methods, weighted sum, weighted by enhanced similarity of the items. This system intend to offer better prediction quality than other approaches and to produce better recommendation results as a result of considering item-demographic similarity with similarity result from explicit rating of the user.