Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation

Trust has been extensively exploited to improve the predictive The dependency between the trust aspects is captured by accuracy of recommendations by ameliorating the issues a Guassian radial basis kernel function. Then, we incorporate such as data sparsity and cold start that recommender the trust information into the probabilistic matrix factorization systems inherently suffer from (Massa and Avesani 2007; model (Mnih and Salakhutdinov 2007) by modeling Ma et al. 2008). Basically, trust provides additional information trust as jointly conditioning on the trust value obtained from which user preference can be better modeled, alternative from the SVR model, as well as similarity between the corresponding or complementary to rating-based similarity.