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Topological Influence-Aware Recommendation on Social Networks

Author

Listed:
  • Zhaoyi Li
  • Fei Xiong
  • Ximeng Wang
  • Hongshu Chen
  • Xi Xiong

Abstract

Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.

Suggested Citation

  • Zhaoyi Li & Fei Xiong & Ximeng Wang & Hongshu Chen & Xi Xiong, 2019. "Topological Influence-Aware Recommendation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-12, February.
  • Handle: RePEc:hin:complx:6325654
    DOI: 10.1155/2019/6325654
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    References listed on IDEAS

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    1. Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 179-186.
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    3. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.
    4. Jinpeng Chen & Wen Zhang & Pei Zhang & Pinguang Ying & Kun Niu & Ming Zou, 2018. "Exploiting Spatial and Temporal for Point of Interest Recommendation," Complexity, Hindawi, vol. 2018, pages 1-16, August.
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    Cited by:

    1. Atdag, Samet & Bingol, Haluk O., 2021. "Computational models for commercial advertisements in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    2. Syed Raza Bashir & Shaina Raza & Vojislav B. Misic, 2023. "BERT4Loc: BERT for Location—POI Recommender System," Future Internet, MDPI, vol. 15(6), pages 1-19, June.
    3. Yao, Yao & Li, Yuanyuan & Xiong, Xi & Wu, Yue & Lin, Honggang & Ju, Shenggen, 2020. "An interactive propagation model of multiple information in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

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