A probabilistic framework of preference discovery from folksonomy corpus

作     者：Xiaohui GUO Chunming HU Richong ZHANG Jinpeng HUAI 

作者机构：State Key Lab of Software Development Environment Beihang University Beijing 100191 China Institute of Advanced Computing Technology School of Computer Science and Engineering Beihang University Beijing 100191 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2017年第11卷第6期

页      面：1075-1084页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：This work was supported by the National Basic Re-search 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 

主　　题：preference discovery tagging folksonomy so-cial annotation 

摘      要：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 re- trieval results. This paper presents a rigorous probabilis- tic framework to discover user preference from folkson- omy 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 applica- bility of the engaged models. The experimental results show that, with the help of the proposed framework, the user pref- erence can be effectively discovered.