A Collaborative Filtering Approach for Movies Recommendation Based on User Clustering and Item Clustering

Abstract

Recommender systems (RS) are software tools that have become increasingly popular in recent years. RS are utilized in a variety of areas including movies, music, news, books, research articles, etc. Typically, there are many items and many users present in these areas making the problem hard and expensive to solve. Collaborative filtering is a widely used approach to design of recommender systems. This method is based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items like movies without requiring an understanding of the item itself. We present a new approach based on user clustering and item clustering to recommendation for the active user. The K-means clustering algorithm is used to categorize users based on their interests. Our result shows that the proposed algorithm provides improved quality of clusters and also render a better recommendation to the users.