Clustering approach to collaborative filtering using social networks

This paper presents results of using clustering to improve results of collaborative filtering. Clusters of users are created using friendship links within a social network using Markov Chain Algorithm (MCL). Clusters are then used to make prediction of user choices using item based collaborative filtering with cosine similarity. Using the results from analyzing different cluster sizes, new algorithm was proposed that saves time and memory resources.