A Novel Recommendation Model Regularized with User Trust and Item Ratings

Guibing Guo, Jie Zhang, Neil Yorke-Smith

Research output: Contribution to journalArticleScientificpeer-review

127 Citations (Scopus)


We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.
Original languageEnglish
Pages (from-to)1607-1620
Number of pages14
JournalIEEE Transactions on Knowledge & Data Engineering
Issue number7
Publication statusPublished - Jul 2016
Externally publishedYes


  • Recommender systems
  • social trust
  • matrix factorization
  • implicit trust
  • collaborative filtering

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