A Topic-Specific Contextual Expert Finding Method in Social Network

摘要：

　　Expert retrieval is a widely studied problem.However,most existing expert finding methods focus on social network which contains topic-irrelevant users and interactions.This results in that the expert results are not topic-specific and practical because many users need to find experts for certain topic.Furthermore,contextual factors of social network also affect the accuracy of expert finding and are seldom concerned comprehensively in existing approaches.To solve above problems,in this paper,we propose a topic-specific contextual expert finding method.At first,we define a topic-specific contextual feature model (TSCFM) which consists of a topic-aware model (TAM) for topical feature and a context-aware model (CAM) for contextual feature.TAM uses LDA and HITS to extract topical feature,and CAM evaluates social relation,time and location factors to extract contextual features.Then based on TSCFM,we learn an expert scoring function which synthetically concerns topical and contextual features using SVM algorithm and rank the experts.The experiments on two datasets demonstrate that our proposed expert finding method is feasible and can improve the accuracy.

关键词： Social network Expert finding Topic-aware Context-aware

作者: Xiaoqin Xie Yijia Li Zhiqiang Zhang Haiwei Pan Shuai Han

作者单位: College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 292-303

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）