Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high migration of users in and out of communities, a key challenge is to design effective strategies for recommending experts for new questions. In this paper, we propose a simple graph-diffusion expert recommendation model for CQA, that can outperform state-of-the art deep learning representatives and collaborative models. Our proposed method learns users' expertise in the context of both semantic and temporal information to capture their changing interest and activity levels with time. Experiments on five real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of ~ 30% performance gain compared to the best baseline method.
翻译:社区问题解答(CQA)网站已成为宝贵的知识库,个人通过询问和回答问题交流信息。随着问题越来越多,用户在社区内外的移徙人数越来越多,关键的挑战是如何设计有效的战略,建议专家提出新的问题。在本文件中,我们为CQA提出了一个简单的图形扩散专家建议模式,该模式可以优于最先进的深层次学习代表和协作模式。我们建议的方法在语义和时间信息方面学习用户的专门知识,以便用时间来捕捉他们不断变化的兴趣和活动水平。 Stack Exchange 网络对五个真实世界数据集的实验表明,我们的方法超越了竞争性基线方法。此外,关于冷启动用户(历史记录有限的用户)的实验显示,与最佳基线方法相比,我们的模式平均达到30%的绩效收益。