Prediction algorithms for collaborative filtering

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. There are two general classes of collaborative filtering algorithms: memory-based methods and model-based methods. This paper firstly analyzes their advantages and shortcomings based on overview of the collaborative filtering process, then especially analyzes three main prediction algorithms. At last, this paper evaluates their prediction capability based on error metric NMAE through experiment on the EachMovie data set.