Study of Collaborative Filtering Recommendation Algorithm Scalability Issue

Abstract

Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information services. Collaborative Filtering technique is the most successful in the recommender systems field. Collaborative filtering creates suggestions for users based on their neighbors preferences. But it suffers from poor accuracy, scalability and cold start problems. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the enhancement of scalability and efficiency of collaborative filtering (CF) algorithms become progressively more important and difficult. This paper focuses on study of different collaborative filtering algorithms taking into consideration the scalability issue. The different algorithms studied are cluster based, item based and context based.