An Improved Hybrid Recommender System Using Multi-Based Clustering Method

抄録

Recommender systems have become an important research area as they provide some kind of intelligent web techniques to search through the enormous volume of information available on the internet. Content-based filtering and collaborative filtering methods are the most widely recommendation techniques adopted to date. Each of them has both advantages and disadvantages in providing high quality recommendations therefore a hybrid recommendation mechanism incorporating components from both of these methods would yield satisfactory results in many situations. In this paper, we present an elegant and effective framework for combining content-based filtering and collaborative filtering methods. Our approach clusters on user information and item information for content-based filtering to enhance existing user data and item data. Based on the result from the first step, we calculate the predicted rating data for collaborative filtering. We then do cluster on predicted rating data in the last step to enhance the scalability of our proposed system. We call our proposal multi-based clustering method. We show that our proposed system can solve a cold start problem, a sparsity problem, suitable for various situations in real-life applications. It thus contributes to the improvement of prediction quality of a hybrid recommender system as shown in the experimental results.