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Combining content and relation analysis for recommendation in social tagging systems

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  • Zhang, Yin
  • Zhang, Bin
  • Gao, Kening
  • Guo, Pengwei
  • Sun, Daming

Abstract

Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.

Suggested Citation

  • Zhang, Yin & Zhang, Bin & Gao, Kening & Guo, Pengwei & Sun, Daming, 2012. "Combining content and relation analysis for recommendation in social tagging systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5759-5768.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:22:p:5759-5768
    DOI: 10.1016/j.physa.2012.05.013
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    References listed on IDEAS

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    1. Shang, Ming-Sheng & Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Collaborative filtering with diffusion-based similarity on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1259-1264.
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    3. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    Cited by:

    1. 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.
    2. Yunhong Xu & Dehu Yin & Duanning Zhou, 2019. "Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users," Information Systems Frontiers, Springer, vol. 21(4), pages 761-772, August.
    3. Hu, Ya-Han & Tai, Chun-Tien & Liu, Kang Ernest & Cai, Cheng-Fang, 2020. "Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity," Journal of Informetrics, Elsevier, vol. 14(1).
    4. 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.

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