Research on Collaborative Filtering Recommendation Based on Trust Relationship and Rating Trust

Authors

  • Wenjun Huang
  • Junyu Chen
  • Yue Ding

Keywords:

Trust relationship, Rating trust, Collaborative filtering, Probability matrix factorization

Abstract

In the Internet age, how to dig out useful information from massive data has become a research hotspot. The emergence of recommendation algorithms effectively solves the problem of information overload, but traditional recommendation algorithms face problems such as data sparseness, cold start, and low accuracy. Later social recommendation algorithms usually only use a single social trust information for recommendation, and the integration of multiple trust relationships lacks an efficient model, which greatly affects the accuracy and reliability of recommendation. This paper proposes a trust-based approach. Recommended algorithm. First, use social trust data to calculate user trust relationships, including user local trust and user global trust. Further based on the scoring data, an implicit trust relationship is calculated, called rating trust, which includes scoring local trust and scoring global trust. Then set the recommendation weight, build the preference relationship between users through user trust and rating trust, and form a comprehensive trust relationship. The trust relationship of social networks is integrated into the probability matrix decomposition model to form an efficient and unified trusted recommendation model TR-PMF. This algorithm is compared with related algorithms on the Ciao and FilmTrust datasets, and the results prove that our method is competitive with other recommendation algorithms.

References

Iman Barjasteh, Rana Forsati, Dennis Ross, Abdol-Hossein Esfahanian, and Hayder Radha. Cold-start recommendation with provable guarantees: A decoupled approach. IEEE Transactions on Knowledge and Data Engineering, 28(6):1462–1474, 2016.

Guang-Neng Hu, Xin-Yu Dai, Feng-Yu Qiu, Rui Xia, Tao Li, Shu-Jian Huang, and Jia-Jun Chen. Collaborative filtering with topic and social latent factors incorporating implicit feedback. ACM Transactions on Knowledge Discovery from Data (TKDD), 12(2):1–30, 2018.

Mohsen Jamali and Martin Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems, pages 135–142, 2010.

Liaoliang Jiang, Yuting Cheng, Li Yang, Jing Li, Hongyang Yan, and Xiaoqin Wang. A trust-based collaborative filtering algorithm for ecommerce recommendation system. Journal of Ambient Intelligence and Humanized Computing, 2019.

Mingyang Jiang, Zhifeng Zhang, Jingqing Jiang, Qinghu Wang, and Zhili Pei. A collaborative filtering recommendation algorithm based on information theory and bi-clustering. Neural Computing and Applications, 2019.

Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009.

Hanjun Lee, Keunho Choi, Donghee Yoo, Yongmoo Suh, Soowon Lee, and Guijia He. Recommending valuable ideas in an open innovation community: A text mining approach to information overload problem. Industrial Management & Data Systems, 2018.

Hao Ma, Irwin King, and Michael R Lyu. Learning to recommend with social trust ensemble. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 203–210, 2009.

Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management, pages 931–940, 2008.

Hao Ma, Dengyong Zhou, Chao Liu, Michael R Lyu, and Irwin King. Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 287–296, 2011.

Steffen Rendle. Factorization machines. In 2010 IEEE International Conference on Data Mining, pages 995–1000. IEEE, 2010.

Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web, pages 111–112, 2015.

Jiliang Tang, Huiji Gao, and Huan Liu. mtrust: Discerning multi-faceted trust in a connected world. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 93–102, 2012.

Weilong Yao, Jing He, Guangyan Huang, and Yanchun Zhang. Modeling dual role preferences for trust-aware recommendation. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 975–978, 2014.

Weiwei Yuan, Donghai Guan, Young-Koo Lee, Sungyoung Lee, and Sung Jin Hur. Improved trust-aware recommender system using smallworldness of trust networks. Knowledge-Based Systems, 23(3):232–238, 2010.

Yuan Zhang, Ke Meng, Weicong Kong, and Zhao Yang Dong. Collaborative filtering-based electricity plan recommender system. IEEE Transactions on Industrial Informatics, 15(3):1393–1404, 2019.

Downloads

Published

2021-04-19

How to Cite

Huang, W., Chen, J., & Ding, Y. (2021). Research on Collaborative Filtering Recommendation Based on Trust Relationship and Rating Trust. Frontiers in Business, Economics and Management, 1(2), 1-9. Retrieved from https://drpress.org/ojs/index.php/fbem/article/view/13

Issue

Section

Articles