Recommender system based on Hierarchical Clustering algorithm Chameleon

Recommender Systems are becoming inherent part of today´s e-commerce applications. Since recommender system has a direct impact on the sales of many products therefore Recommender system plays an important role in e-commerce. Collaborative filtering is the oldest techniques used in the recommender system. A lot of work has been done towards the improvement of collaborative filtering which comprises of two components User Based and Item Based. The basic necessity of today´s recommender system is accuracy and speed. In this work an efficient technique for recommender system based on Hierarchical Clustering is proposed. The user or item specific information is grouped into a set of clusters using Chameleon Hierarchical clustering algorithm. Further voting system is used to predict the rating of a particular item. In order to evaluate the performance of Chameleon based recommender system, it is compared with existing technique based on K-means clustering algorithm. The results demonstrates that Chameleon based Recommender system produces less error as compared to K-means based Recommender System.