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An enhanced social matrix factorization model for recommendation based on social networks using social interaction factors

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Abstract

Recommender systems are recently becoming more significant in the age of rapid development of Internet technology and pervasive computing due to their ability in making appropriate choices to users. Collaborative filtering is one of the most successful recommendation techniques, which recommends items to an active user based on past ratings from like-minded users. However, the user-item rating matrix, namely one of the inputs to the recommendation algorithm, is often highly sparse, thus collaborative filtering may lead to the poor recommendation. To solve this problem, social networks can be employed to improve the accuracy of recommendations. Some of the social factors have been used in recommender system, but have not been fully considered. In this paper, we fuse personal cognition behavior, cognition relationships between users, and time decay factor for rated items into a unified probabilistic matrix factorization model and propose an enhanced social matrix factorization approach for personalized recommendation using social interaction factors. In this study, we integrate propagation enhancement, common user relationship enhancement, and common interest enhancement into social relationship between users, and propose a novel trust relationship calculation to alleviate the negative impact of sparsity of data rating. The proposed model is compared with the existing social recommendation algorithms on real world datasets including the Epinions and Movielens datasets. Experimental results demonstrate that our proposed approach achieves superior performance to the other recommendation algorithms.

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Notes

  1. https://movielens.org

  2. http://www.epinions.com

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Acknowledgments

We would like to thank Quanli Gao from our group and Weili Guan from Hewlett Packard enterprise Singapore for their suggestions on revising this paper and their friendly support of the research. In addition, we would like to thank the anonymous reviewers and editor for their helpful comments.

This work was supported in part by the National Natural Science Foundation of China under Grants 61975187, and 61503206, in part by joint funded projects of the Special Scientific Research Fund for doctoral program of Higher Education under Grant 20126101110006, in part by the Blue Book of Science Research Report on the “Belt and Road” Tourism Development Grant 2017sz01, in part by Shaanxi innovation capability support plan under Grant 2018KRM071, in part by the Industrial Science and Technology Research Project of Shaanxi Province under Grant 2016GY-123 in part by the Industrial Science and Technology Research Project of Henan Province under Grants 202102210387, and 182102310969.

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Authors and Affiliations

  1. Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Rui Chen

  2. Insititute for Silk Road Research, Xi’an University of Finance and Economics, Xi’an, 710100, China

    Yan-Shuo Chang

  3. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Qingyi Hua, Xiang Ji & Bo Wang

  4. College of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China

    Quanli Gao

  5. School of Computer, Xi’an University of Posts & Telecommunications, Xi’an, 710121, China

    Bo Wang

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  1. Rui Chen
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Correspondence to Yan-Shuo Chang or Qingyi Hua.

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Chen, R., Chang, YS., Hua, Q. et al. An enhanced social matrix factorization model for recommendation based on social networks using social interaction factors. Multimed Tools Appl 79, 14147–14177 (2020). https://doi.org/10.1007/s11042-020-08620-3

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