A probabilistic framework of preference discovery from folksonomy corpus

doi:10.1007/s11704-016-5132-3

Front. Comput. Sci.

2017, Vol. 11

Issue (6) : 1075-1084 https://doi.org/10.1007/s11704-016-5132-3

RESEARCH ARTICLE

A probabilistic framework of preference discovery from folksonomy corpus

Xiaohui GUO^1,², Chunming HU^1,²(

), Richong ZHANG^1,², Jinpeng HUAI^1,²

¹. State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China
². Institute of Advanced Computing Technology, School of Computer Science and Engineering, Beihang University, Beijing 100191, China

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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.

Keywords preference discovery tagging folksonomy social annotation

Corresponding Author(s): Chunming HU

Just Accepted Date: 25 December 2015 Online First Date: 18 July 2016 Issue Date: 07 December 2017

Cite this article:

Xiaohui GUO,Chunming HU,Richong ZHANG, et al. A probabilistic framework of preference discovery from folksonomy corpus[J]. Front. Comput. Sci., 2017, 11(6): 1075-1084.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5132-3
https://academic.hep.com.cn/fcs/EN/Y2017/V11/I6/1075

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