Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships

TL;DR

This paper proposes merging matrix factorization-based collaborative filtering with social friendship data to improve recommendation accuracy, validated through experiments on a real social network showing enhanced results.

Contribution

It introduces a novel method combining social relationships with matrix factorization for collaborative filtering, enhancing recommendation quality.

Findings

Improved recommendation accuracy over traditional CFSs.

Effective integration of social friendship data.

Validated on real-world social network data.

Abstract

Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated by Collaborative Filtering Systems (CFSs). Traditional CFSs based on Matrix Factorization operate on the ratings provided by users and have been recently extended to incorporate demographic aspects such as age and gender. In this paper we propose to merge CFS based on Matrix Factorization and information regarding social friendships in order to provide users with more accurate suggestions and rankings on items of their interest. The proposed approach has been evaluated on a real-life online social network; the experimental results show an improvement against existing CFSs. A detailed comparison with related literature is also present.