论文标题
基于主题社区的问题路由的时间专业知识
Topic Community Based Temporal Expertise for Question Routing
论文作者
论文摘要
基于社区的问题回答网站中的问题路由旨在向最有可能提供“接受答案”的潜在用户推荐新发布的问题。大多数现有方法基于过去的问题回答行为和新问题的内容来预测用户的专业知识。但是，这些方法在三个方面面临着挑战：1）用户过去的记录的稀疏性导致缺乏个性化的建议，即有时不符合用户的兴趣或域专业知识，2）基于所有问题和答案的建模，基于所有问题和答案的内容使定期更新计算昂贵，而3）CQA站点高度动态，它们被认为是静态的。本文提出了一种针对上述挑战的新型QR方法。它基于用户对主题社区活动的动态建模。三个现实世界数据集的实验结果表明，所提出的模型显着优于竞争基线模型
Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide "accepted answers". Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: 1) sparsity of users' past records results in lack of personalized recommendation that at times does not match users' interest or domain expertise, 2) modeling based on all questions and answers content makes periodic updates computationally expensive, and 3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users' activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models
