A SURVEY OF MEMORY BASED METHODS FOR COLLABORATIVE FILTERING BASED TECHNIQUES FOR ONLINE RECOMMENDER SYSTEMS

The cyberspace aims at providing an increasingly dynamic experience to users. The rise of electronic commerce has led to efforts for providing a highly efficient and qualitative experience to the consumer. Recommender Systems are a step in this direction. They aid in understanding the unlimited amount of data available and in particularly knowing each user. One of the most flourishing techniques to generate recommendations is Collaborative filtering. The technique focuses on using available information about existing users to generate prediction for the active user. A widely employed approach for the purpose is the memory based algorithm. The existing preferences of a user are represented in form of a useritem matrix. The method makes use of the complete or partial user-item matrix in order to isolate the nearest users for the active user and then generate the prediction. The majority of initial efforts dedicated to understanding electronic commerce and recommender systems concentrate only on the technical aspects like algorithm building and computational needs of such systems. Not much attention has been provided to questions pertaining to the need of such systems or how effective they are at what they try to perform. Along with looking at the various stages corresponding to a memory based collaborative filtering system, we propose an experiment to check the effectiveness of predictions or ratings generated by such systems.