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Utilizing the influence of multiple potential factors for social recommendation

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Abstract

The recommendation system helps users select satisfactory products and services to make reasonable decisions. In recent years, most methods have introduced social information into the recommendation system to improve recommendation accuracy. Most social recommendations only consider that users are affected by historical items and social information. But users are affected by multiple potential factors that cannot be explicitly distinguished when making decisions, such as consumption experience and life cycle related to users, social consumption culture, and social roles related to social friends, and they are mixed with each other. To describe the above scenario, we propose a model that Utilizes the influence of multiple potential factors for Social Recommendation (UimSRec). Specifically, we simulate the influence of different potential factors on users in the form of latent semantics, associate the potential influence with user representation, and use the user latent representation to model preferences. In addition, this paper uses the attention mechanism to adaptively assign weights to multiple influencing factors and common influencing factors. The experimental results on three real datasets show that modeling various potential impact information and their relationships can significantly improve the recommendation performance. The code is available at (https://github.com/qinkaili/UimSRec).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61876001) and the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (KJ2021A0039). In addition, we acknowledge the High-performance Computing Platform of Anhui University for providing computing resources.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, 230039, Anhui, China

    Fulan Qian, Kaili Qin, Hai Chen, Jie Chen, Shu Zhao, Peng Zhou & Yanping Zhang

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Fulan Qian, Kaili Qin, Hai Chen, Jie Chen, Shu Zhao, Peng Zhou & Yanping Zhang

  3. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei, China

    Fulan Qian, Kaili Qin, Hai Chen, Jie Chen, Shu Zhao & Yanping Zhang

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  1. Fulan Qian
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  2. Kaili Qin
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  3. Hai Chen
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  4. Jie Chen
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  5. Shu Zhao
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Correspondence to Fulan Qian.

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Qian, F., Qin, K., Chen, H. et al. Utilizing the influence of multiple potential factors for social recommendation. Knowl Inf Syst 65, 4213–4232 (2023). https://doi.org/10.1007/s10115-023-01883-w

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