融合矩阵分解与距离度量学习的社会化推荐算法

doi:10.11896／j.issn.1002-137X.2018.10.036

Abstract

Abstract: In order to solve the dilemma called cold start in traditional recommender systems,a novel social recommendation method integrating matrix factorization and distance metric learning was proposed based on the assumption that distance reflects likability.The algorithm trains the samples and distance metric,at the same time,the distance metric and the coordinates of users and items are updated to meet the constraints of distance.Finally,users and items are embedded into an united low dimensional space,and the distance between users and items is used to generate recommendation results.The experimental results on Douban and Epinions datasets show that the proposed method can effectively improve both interpretability and accuracy of recommender systems and is superior to recommendation methods based on matrix factorization.Research results indicate that the proposed method mitigates the cold start dilemma intraditionalrecommender systems,and it provides another research idea for recommender systems.

Key words: Collaborative filtering, Distance metric learning, Matrix factorization, Social recommendations

CLC Number:

TP391

WEN Jun-hao, DAI Da-wen, YU Jun-liang, GAO Min, ZHANG Yi-hao. Social Recommendation Method Integrating Matrix Factorization and Distance Metric Learning[J].Computer Science, 2018, 45(10): 196-201.

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Social Recommendation Method Integrating Matrix Factorization and Distance Metric Learning