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Effective mechanism for social recommendation of news

Author

Listed:
  • Wei, Dong
  • Zhou, Tao
  • Cimini, Giulio
  • Wu, Pei
  • Liu, Weiping
  • Zhang, Yi-Cheng

Abstract

Recommender systems represent an important tool for news distribution on the Internet. In this work we modify a recently proposed social recommendation model in order to deal with no explicit ratings of users on news. The model consists of a network of users which continually adapts in order to achieve an efficient news traffic. To optimize the network’s topology we propose different stochastic algorithms that are scalable with respect to the network’s size. Agent-based simulations reveal the features and the performance of these algorithms. To overcome the resultant drawbacks of each method we introduce two improved algorithms and show that they can optimize the network’s topology almost as fast and effectively as other not-scalable methods that make use of much more information.

Suggested Citation

  • Wei, Dong & Zhou, Tao & Cimini, Giulio & Wu, Pei & Liu, Weiping & Zhang, Yi-Cheng, 2011. "Effective mechanism for social recommendation of news," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2117-2126.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:11:p:2117-2126
    DOI: 10.1016/j.physa.2011.02.005
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    Citations

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    Cited by:

    1. Geng, Bingrui & Li, Lingling & Jiao, Licheng & Gong, Maoguo & Cai, Qing & Wu, Yue, 2015. "NNIA-RS: A multi-objective optimization based recommender system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 383-397.
    2. Aziz, Furqan & Gul, Haji & Muhammad, Ishtiaq & Uddin, Irfan, 2020. "Link prediction using node information on local paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Zhang, Yin & Gao, Kening & Zhang, Bin, 2015. "The concept exploration model and an application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 430-442.
    4. Tao Zhou & Matúš Medo & Giulio Cimini & Zi-Ke Zhang & Yi-Cheng Zhang, 2011. "Emergence of Scale-Free Leadership Structure in Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-6, July.
    5. Xu, Jinghong & Zhang, Lin & Ma, Baojun & Wu, Ye, 2016. "Impacts of suppressing guide on information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 922-927.
    6. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    7. Zhao, Narisa & Cui, Xuelian, 2017. "Impact of individual interest shift on information dissemination in modular networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 232-242.
    8. Yin, Chun-Xia & Peng, Qin-Ke & Chu, Tao, 2012. "Personal artist recommendation via a listening and trust preference network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 1991-1999.
    9. Sun, Xin & Dong, Junyu & Tang, Ruichun & Xu, Mantao & Qi, Lin & Cai, Yang, 2015. "Topological evolution of virtual social networks by modeling social activities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 259-267.
    10. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.

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