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Multi-affect(ed): improving recommendation with similarity-enhanced user reliability and influence propagation

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Abstract

Traditional recommendation algorithms predict the latent interest of an active user by collecting rating information from other similar users or items. Recently, more and more recommendation systems attempt to involve social relations to improve recommendation performance. However, the existing works either leave out the user reliability or cannot capture the correlation between two users who are similar but not socially connected. Besides, they also take the trust value between users either 0 or 1, thus degenerating the prediction accuracy. In this paper, we propose an efficient social affect model, multi-affect(ed), for recommendation via incorporating both users’ reliability and influence propagation. Specifically, the model contains two main components, i.e., computation of user reliability and influence propagation, designing of user-shared feature space. Firstly, a reliability calculation strategy based on user similarity is developed for measuring the recommendation accuracy between users. Then, the factor of influence propagation relationship among users is taken into consideration. Finally, the multi-affect(ed) model is developed with user-shared feature space to generate the predicted ratings.

Experimental results demonstrate that the proposed model achieves better accuracy than other counterparts recommendation techniques.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61762078, 61363058, 61966009, 61762079, U1711263, U1811264), Guangxi Key Laboratory of Trusted Software (kx202003) and Major Project of Young Teachers’ Scientific Research Ability Promotion Plan (NWNU-LKQN2019-2).

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Authors and Affiliations

  1. College of Computer science and engineering, Northwest Normal University, Lanzhou, 730070, China

    Lele Huang, Huifang Ma & Xiangchun He

  2. College of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Huifang Ma & Liang Chang

Authors
  1. Lele Huang
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  2. Huifang Ma
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  3. Xiangchun He
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  4. Liang Chang
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Corresponding author

Correspondence to Huifang Ma.

Additional information

Lele Huang postgraduate in the College of Computer Science and Engineering, Northwest Normal University, China. Her research interest covers intelligent recommendation and data mining.

Huifang Ma is a professor in the College of Computer Science and Engineering, Northwest Normal University, China. She received her PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. Her research interest covers artificial intelligence, data mining, and machine learning.

Xiangchun He is a associate professor in the College of Education Technology, Northwest Normal University, China. His research interest covers information technology and education application research.

Liang Chang is a professor in the School of Computer Science and Information Security, Guilin University of Electronic Technology, China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2008. His research interest covers data and knowledge engineering, intelligent recommendation system, and formal methods.

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Huang, L., Ma, H., He, X. et al. Multi-affect(ed): improving recommendation with similarity-enhanced user reliability and influence propagation. Front. Comput. Sci. 15, 155331 (2021). https://doi.org/10.1007/s11704-020-9511-4

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  • DOI: https://doi.org/10.1007/s11704-020-9511-4

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