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 by Combining User Topics and Behaviors

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。

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 topicsensitive 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.