A Scalable Collaborative Filtering Based Recommender System Using Incremental Clustering

Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the users. Content-based filtering and collaborative filtering are usually applied to predict these recommendations. Among these two, Collaborative filtering is the most common approach for designing e-commerce recommender systems. Two major challenges for CF based recommender systems are scalability and sparsity. In this paper we present an incremental clustering approach to improve the scalability of collaborative filtering.