A Novel Recommendation Model Regularized with User Trust and Item Ratings
Abstract— A Novel Recommendation Model Regularized with User Trust and Item Ratings. 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++ < Final Year Projects 2016 > 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.
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