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A probabilistic framework of preference discovery from folksonomy corpus

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Abstract

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.

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Acknowledgements

This work was supported by the National Basic Research program of China (2014CB340305), partly by the National Natural Science Foundation of China (Grant Nos. 61300070 and 61421003) and partly by the State Key Lab for Software Development Environment.

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Authors and Affiliations

  1. State Key Lab of Software Development Environment, Beihang University, Beijing, 100191, China

    Xiaohui Guo, Chunming Hu, Richong Zhang & Jinpeng Huai

  2. Institute of Advanced Computing Technology, School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Xiaohui Guo, Chunming Hu, Richong Zhang & Jinpeng Huai

Authors
  1. Xiaohui Guo
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  2. Chunming Hu
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  3. Richong Zhang
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  4. Jinpeng Huai
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Corresponding author

Correspondence to Chunming Hu.

Additional information

Xiaohui Guo is a PhD student in the School of Computer Science and Engineering, Beihang University, China. His research interests include machine learning, data mining, and services oriented computing. His researches of machine learning are mainly focused on probabilistic graphical model, large-scale Bayesian model inference, and Bayesian sparse learning. Data mining researches include computational advertisements, recommender system, and geospatial temporal data analysis.

Chunming Hu is an associate professor at School of Computer Science and Engineering, Beihang University, China. He received his PhD degree from Beihang University in 2006. He was a post-doctoral research fellow in the distributed system group, Hong Kong University of Science and Technology, China in 2006–2007. His current research interests include the distributed systems, system virtualization, large scale data management and processing systems, and data mining applications.

Richong Zhang received his BS degree and MA degree from Jilin University, China in 2001 and 2004. In 2006, he received his MS degree from Dalhousie University, Canada. He received his PhD from the School of Information Technology and Engineering, University of Ottawa, Canada. He is currently an associate professor in the School of Computer Science and Engineering, Beihang University, China. His reasearch interests include recommender systems, knowledge graph and crowdsourcing.

Jinpeng Huai is a professor of the School of Computer Science and Engineering at Beihang University, China and a vice-minister of Ministry of Industry and Information Technology of the People’s Republic of China. He is an academician of Chinese Academy of Sciences. He used to serve on the Steering Committee for Advanced Computing Technology Subject for the National High-Tech Program (863) as Chief Scientist. His research interests include big data computing, distributed system, virtual computing, service-oriented computing, trustworthiness and security.

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Guo, X., Hu, C., Zhang, R. et al. A probabilistic framework of preference discovery from folksonomy corpus. Front. Comput. Sci. 11, 1075–1084 (2017). https://doi.org/10.1007/s11704-016-5132-3

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  • DOI: https://doi.org/10.1007/s11704-016-5132-3

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