基于用户-标签异构网络的社区问答专家发现方法  

Expert Discovery Method Based on User-Tag Heterogeneous Network for Community Question Answering

作　　者：黄辉 刘永坚[1] 解庆 HUANG Hui;LIU Yongjian;XIE Qing(School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学计算机科学与技术学院

出　　处：《计算机工程》2020年第2期53-58,共6页Computer Engineering

基　　金：国家自然科学基金(61602353)

摘　　要：在Stack Overflow、Quora等社区问答网站中,日益增长的用户数使新问题数量急剧增加,传统的专家发现方法通常根据历史回答记录建立用户文档,再从中提取用户文本特征,难以及时寻找到合适的专家进行回答。针对该问题,提出一种社区问答中基于用户-标签异构网络的专家发现方法。根据用户历史回答记录和问题的附带标签构建用户-标签网络,以此得到用户的向量表示。在此基础上,使用全连接神经网络提取用户特征和问题文本特征,通过比较两者的余弦相似度得到候选专家列表。基于StackExchange的真实世界数据集进行测试,实验结果表明,与LDA、STM、RankingSVM和QR-DSSM方法相比,该方法的MRR指标值较高,能够准确寻找到可提供正确答案的专家。In Community Question Answering(CQA)websites such as Stack Overflow and Quora,the growing number of users leads to a sharp increase in the number of new questions.Traditional expert discovery methods usually establish user documents based on historical answer records and extract user text features from them,making it difficult to find appropriate experts to answer questions in time.To address this problem,an expert discovery method based on user-tag heterogeneous network is proposed.This method builds a user-tag network based on the historical answer records of users and tags attached to the questions,so as to obtain the vector representation of users.On this basis,fully connected neural network is used to extract user features and text features of questions,and their cosine similarity is compared to obtain the list of candidate experts.Experimental results on the real world dataset of stackExchange show that compared with LDA,STM,RankingSVM and QR-DSSM methods,this method has a higher index value,and can accurately find experts that can provide correct answers.

关 键 词：社区问答 专家发现 问题路由 深度学习 网络嵌入 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]