ABSTRACT

the most common technique used for recommendations is collaborative filtering. Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships from a group of user who share the same preferences and taste. In this paper we have explored various aspects of collaborative filtering recommendation system. We have categorized collaborative filtering recommendation system and shown how the similarity is computed. The desired criteria for selection of data set are also listed. The measures used for evaluating the performance of collaborative filtering recommendation system are discussed along with the challenges faced by the recommendation system. Types of rating that can be collected from the user to rate items are also discussed along with the uses of collaborative filtering recommendation system.