A Novel Recommendation Model Regularized with User Trust and Item Ratings
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A Novel Recommendation Model Regularized with User Trust and Item Ratings
Abstract—We propose Trust SVD, 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. < final year projects >
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