Matrix factorization based social recommender model
YAN Surong1,2, FENG Xiaoqing1, LIAO Yixing1
1. College of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China;
2. Department of Electrical Engineering & Computer Science, University of California, Irvine, California 92697-2625, USA
Abstract：This study describes an improved matrix factorization based social recommender model that uses tailored relationship networks of users as a solution for the sparsity, cold-start and scalability problems in big datasets. The social influence of the relationship networks is targeted as an extra user-item specific bias for the matrix factorization with the uniformity of relationship networks modeled as dynamic social regularization terms in the matrix factorization. A boosting-shrinking algorithm is used for the relationship networks for better prediction accuracy and scalability where the relationships of each user are tailored to generate personalized relationship networks according to the user-specific data density of the user-item rating matrix and the correlation matrix. Tests on unbalanced datasets with different sparsity levels show that this model significantly improves the prediction accuracy for sparse datasets, effectively addresses the cold-start problem, and has better scalability compared to other state-of-the-art matrix factorization based social recommendation models.
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