Computer Science ›› 2017, Vol. 44 ›› Issue (4): 288-294.doi: 10.11896/j.issn.1002-137X.2017.04.059

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Using Social Trust Relationship and Helpfullness Ratings for Recommendation Based on Matrix Factorization

ZENG An and XU Xiao-qiang   

  • Online:2018-11-13 Published:2018-11-13

Abstract: So far,cold-start and data sparsity issues have still been two challenges in recommender systems.Most of traditional recommender systems based on the matrix factorization model often assumed that users are isolated and the relationships among users are ignored,this results in the decrease in the recommendation effects.Thus,a novel approach incorporating social trust relationship and helpfulness ratings was proposed.Based on the probabilistic matrix factorization,approach combined the social trust relationships among users with helpfulness ratings was proposed,and the alternating least squares was employed to train model parameters.The experiment results on Epinions and Ciao data set suggested that the proposed approach was superior to other advanced approaches in accuracy and reliability,especially while the cold-start issues were involved in.

Key words: Recommender system,Helpfulness ratings,Trust relationship,Matrix factorization

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