The social network role in improving recommendation performance of collaborative filtering

Abstract

Recently a recommender system has been applied to solve several different problems that face the users. Collaborative filtering is the most commonly used and successfully deployed recommendation technique. Despite everything, the traditional collaborative filtering (TCF) operates only in the two-dimensional user-item space. The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised as an extra source for assisting in the recommendation process. The purpose of this paper is to give an overview of collaborative filtering (CF) and existing methods used social network information to incorporate in collaborative filtering re-commender systems to improve performance and accuracy. We classify CF-based social network information into two categories: TCF-based trust relation approaches and TCF-based friendship relation approaches. For each category, we review the fundamental concept of methods that can be used to improve recommendation performance.