The Social Network Role in Improving Recommendation Performance of Collaborative Filtering

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

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 recommender 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.

Keywords

Recommender systems Collaborative filtering Social networks 

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Notes

Acknowledgments

This work is supported by the Ministry of Higher Education (MOHE) and Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM) under Research University Grant Category (VOT Q.J130000.2528.02H99).

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruMalaysia
  2. 2.Faculty of Pure and Applied ScienceInternational University of AfricaKhartoumRepublic of the Sudan

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