Improving the recommendation accuracy of TrustSVD via trustworthy analysis in the social network environment

Abstract

Recommender systems help Internet users quickly find information they may be interested in from an extremely large amount of resources. Recent studies have shown that incorporating auxiliary social trust relationship information into the recommender system improves the accuracy of recommendations. Most existing research only considers explicit trust relationships, which result in sub-optimal recommendation performance. In this research, we present a trust model which analyses user trustworthiness based on user’s behaviours on the social networks. The proposed trust model increases the density of trust relationships by considering explicit and implicit social trust relationships and also reflects a more fine-grained and realistic trust level between users. This improved social trust information is then incorporated into TrustSVD, a matrix factorisation–based social recommendation method. By analysing the prediction result using a real-world data set, Douban-600k from the Douban Movie website, we found that our proposed method provides more accurate predictions compared with SVD++ and traditional TrustSVD, improving users’ experiences.