Top-N Recommendation Based on Mutual Trust and Influence


  • Dewen Seng School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Jiaxin Liu School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Xuefeng Zhang School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Jing Chen School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Xujian Fang School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China


mutual trust, mutual influence, social recommendation system, cold start, data sparsity


To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach.


Callebert, L.; Lourdeaux, D.; BarthA¨s, J.P. (2018). Collective activity and autonomous characters: trust-based decision-making system, Revue d'Intelligence Artificielle, 31(1-2), 153-181, 2018.

Coste, B.; Ray, C.; Coatrieux, G. (2017). Trust modelling and measurements for the security of information systems, IngAŠnierie des SystA¨mes d'Information, 22(1), 19-41, 2017.

Fang, H.; Bao, Y.; Zhang, J. (2014). Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation, Twenty-Eighth AAAI Conference on Artificial Intelligence, 30-36, 2014.

Guo, X.; Yin, S.; Zhang, Y.; Li, W.; He, Q. (2019). Cold start recommendation based on attribute-fused singular value decomposition, IEEE Access, 7, 11349-11359, 2019.

Guo G.; Zhang J.; Yorke-Smith N (2016). A Novel Recommendation Model Regularized with User Trust and Item Ratings, IEEE Transactions on Knowledge & Data Engineering, 28(7), 1607-1620, 2016.

Guo G.; Zhang J.; Zhu F. et al (2017). Factored similarity models with social trust for top-N item recommendation, Knowledge-Based Systems, 122, 17-25, 2017.

Guo G(2019). List of Recommendation Data Sets,, 2011/6-2013/11.

Han, Z.M.; Chen, Y.; Liu, W.; Yuan, B.H.; Li, M.Q.; Duan, D.G. (2017). Research on node influence analysis in social networks, Journal of Software, 28(1): 84-104, 2017.

Jamali, M.; Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks, ACM Conference on Recommender Systems, 135-142, 2010.

Kabbur, S.; Ning, X.; Karypis, G. (2013). Fism: factored item similarity models for top-n recommender systems, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 659-667, 2013.

Li, W.; Ye, Z.; Xin, M.; Jin, Q. (2017). Social recommendation based on trust and influence in SNS environments, Multimedia Tools & Applications, 76(9), 11585-11602, 2017.

Moradi, P.; Ahmadian, S. (2015). A reliability-based recommendation method to improve trust-aware recommender systems, Expert Systems with Applications, 42(21), 7386-7398, 2015.

Pan, W.; Chen, L. (2013). GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering, Twenty-Third International Joint Conference on Artificial Intelligence, 2691-2697.

Pan, Y.; He, F.; Yu, H. (2018). Social recommendation algorithm using implicit similarity in trust, Chinese Journal of Computers, 41(1), 65-81, 2018.

Rendle, S.; Freudenthaler, C.; Gantner, Z.; Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback, Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, 452-461, 2009.

Tang, J.; Gao, H.; Liu, H.; Sarmas, A.D. (2012). eTrust: Understanding trust evolution in an online world, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 253-261.

Wu, M.X.; Dong, L.S.; Jie, Z.Y.; Hu, X. (2015). Research on social recommender systems, Journal of Software, (6), 1356-1372, 2015.

Wang, M.; Ma, J. (2016). A novel recommendation approach based on users' weighted trust relations and the rating similarities, Soft Computing, 20(10), 3981-3990, 2016.

Wang, Q.; Wang, J.H. (2015). Collaborative filtering recommendation algorithm combining trust mechanism with user preferences, Computer Engineering and Applications, 51(10), 261-265, 2015.

Yang, X.; Guo, Y.; Liu, Y.; Steck, H. (2014). A survey of collaborative filtering based social recommender systems, Computer Communications, 41, 1-10, 2014.

Yao, Q.; Shi, R.; Zhou, C.; Wang, P.; Guo, L. (2016). Topic-aware social influence minimization, Proceedings of the 24th International Conference on World Wide Web, 139-140 2015.

Zhao, F.; Guo, Y. (2016). Improving Top-N recommendation with heterogeneous loss, International Joint Conference on Artificial Intelligence, 2378-2384, 2016.

Zhao, H.Y.; Hou, J.D.; Chen, Q.K. (2015). Collaborative filtering recommendation algorithm combining time weight and trust relationship, Application Research of Computers, 32(12), 3565-3568, 2015.

Zhang, D.; Sui, J.; Gong, Y. (2017). Large scale software test data generation based on collective constraint and weighted combination method, Tehnicki Vjesnik, 24(4), 1041-1050, 2017.

Zhang, J.; Tang, J.; Li, J.; Liu, Y.; Xing, C.X. (2015). Who influenced you? Predicting retweet via social influence locality, ACM Transactions on Knowledge Discovery from Data (TKDD), 9(3), 25, 2015.



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