一种融合话题和行为的在线问答社区领域专家发现方法

doi:10.3969/j.issn.1006-2475.2018.09.015

计算机与现代化

一种融合话题和行为的在线问答社区领域专家发现方法

(北京交通大学计算机与信息技术学院,北京100044)

收稿日期:2018-03-14 出版日期:2018-09-29 发布日期:2018-09-30
作者简介:李科霖(1993-)，男，河南驻马店人，北京交通大学计算机与信息技术学院硕士研究生，研究方向：数据与知识工程。
基金资助:
国家自然科学基金资助项目(61603028)

Discovering Domain Experts in Online Q&A Communities

(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China)

Received:2018-03-14 Online:2018-09-29 Published:2018-09-30

摘要/Abstract

摘要： 根据在线问答社区中答案的产生过程，提出一种“问题-回答者-话题”(Question-Answerer-Topic, QAT)模型，对“问题-回答者对”(question-answerer pair)的领域话题分布进行建模，并结合社区中的点赞行为，融入用户在每个问题下答案的获赞数据，计算用户在领域话题分布下的专业水平，最后结合链接分析的方法，提出一种主题敏感的PageRank改进模型，最终得到每位用户在领域话题下的专家得分。基于中文在线问答社区知乎网的人工智能领域真实数据集进行实验和对比分析，实验结果表明，本文提出的领域专家发现方法明显优于其他现有方法。

关键词: 在线问答社区, 话题模型, 链接分析, 专家发现

Abstract: In this paper, we propose a question-answerer-topic model according to the generation procedure of answers in online Q&A communities, to model the topic distribution of question-answerer pairs. Then we calculate the professional level of users under different topics by incorporating the voting number of each answer in each question. Finally we propose an improved topic-sensitive PageRank model based on the thinking of link analysis to calculate the final expert score of each user under specific topics. The experiments and comparative analysis based on a real data set from a Chinese online Q&A community Zhihu in the field of artificial intelligence are carried out. The experimental results show that the proposed method obviously outperforms other existing expert finding methods.

Key words: online Q&A communities, topic model, link analysis, experts finding

中图分类号:

TP391

李科霖. 一种融合话题和行为的在线问答社区领域专家发现方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2018.09.015.

LI Ke-lin. Discovering Domain Experts in Online Q&A Communities[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.09.015.

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一种融合话题和行为的在线问答社区领域专家发现方法

Discovering Domain Experts in Online Q&A Communities

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