User Based Collaborative Filtering for Music Recommendation System

Recommender systems have been proven to be valuable means for web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce.  The recommendations provided are aimed at supporting their users in various decision making process, such as what items to buy. In this paper we recommend items to users based on their logs. First we use collaborative filtering method to identify the users who are similar based on their listening history. Then recommend the items to new users based on the user clusters formed. At last we have evaluated the performance of the algorithm and propose the ideas that improve the recommendations.