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Recommendation algorithm of influence and trust relationship

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Abstract

The recommendation system recommends information and services to users by collecting and analyzing user behaviors. Many current studies have shown that recommendation algorithms that integrate social network information can effectively improve recommendation performance. Most of the existing social recommendation algorithms assume that the trust relationship between users is singular and homogeneous. These social recommendation algorithms generally ignore two problems: (i) in a network of trust relationships, each user has various friends and trust relationships, which have an impact on user ratings. (ii) each user with different social status, which influences also affects the ratings between users. Propose a social network recommendation algorithm (Social Strength Trust Recommendation Algorithm, SSTRA) in this paper. Firstly, the algorithm uses the different out-degree and in-degree relationships among different users to calculate the different trust strengths of each user in social networks; secondly, it calculates the social influence of different users through the social ranking algorithm (SocailRank); thirdly, it will be based on the trust strength relationship of social networks and the social influence of users are integrated into the probability matrix factorization model. This method can achieve the purpose of optimizing recommendation results. The experimental results compared on the CiaoDVD dataset show that: Compared with the SocialMF, SoRec, RSTE, PMF, and Trust algorithms, the average MAE has increased by 1.33%, 1.69%, 4.88%, 11.17% and 220.41%, and the average RMSE has increased by 1.47%, 1.9%, 5.06%, 7.27%, 217.55%. The experimental results compared on the Ciao dataset show that: Compared with the SocialMF, SoRec, RSTE, PMF, and Trust algorithms, the average MAE is increased by 4.83%, 5.05%, 1.96%, 5.58%, 143.39%, and the average RMSE is increased by 1.76%, 2.17%, 2.1%, 2.38%, 151.1%. Experimental results show that the algorithm has obvious advantages in recommendation accuracy.

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Funding

This research was funded by Jiangsu University of Technology (Grant No. KYY19042).

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Authors and Affiliations

  1. School of Computer Engineering, Jiangsu University of Technology, Jiangsu, 213001, Changzhou, China

    Zhang Li

  2. People’s Bank of China Changzhou City Center Branch, Jiangsu, 213001, Changzhou, China

    Chen XiaoBo

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  1. Zhang Li
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Correspondence to Zhang Li.

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Li, Z., XiaoBo, C. Recommendation algorithm of influence and trust relationship. Multimed Tools Appl 81, 15635–15652 (2022). https://doi.org/10.1007/s11042-022-12231-5

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