Abstract-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 M u s i c R e c o m m e n d a t i o n S y s t e m , we recommend items to users based on their interest. First we use collaborative filtering method to identify the i t e m s which are similar and similarity among users based on the users listening history. P r o p o s e d A l g o r i t h m recommend the items to new users based on the item clusters and user clusters formed. L a t e r we have taken timestamp of user logs also into consideration to form Sessions. Finally we have evaluated the performance of the proposed algorithm with sessions and with -out sessions . Our experiment show that the accuracy of recommendation system with sessions outperformed the conventional user-based & item-based collaborative filtering method.
Because of the revolution in the field of Internet and Ecommerce, users are overwhelmed by choices either it may be a book or movie or Music etc. Recommendations systems are serving as one of the important tool to handle information overloading by providing recommendations to users. In this paper we proposed a method to handle music recommendation problem. Unsupervised discretization is used to cluster the items which are similar in their frequency distribution. The proposed method is evaluated by using a benchmark dataset Last.fm. the results depict the fact that the proposed method performs better than the traditional popular recommendation approach.
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