Collaborative filtering with automatic rating for recommendation

Abstract

In this paper we describe recommender system designed to suggest new products to Web shopping mall customers. The recommender is meant to provide alternatives or new products that suits active user's tastes and meets his/her needs. Most previously proposed recommendation systems that use collaborative filtering can cause a problem when there are insufficient user ratings. We address this problem combining content-based filtering and collaborative filtering. And using automatic rating method instead of users' explicit rating inaccuracy of rating data is decreased.