论文标题
通过重新访问图扩散来简化稀疏专家建议
Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion
论文作者
论文摘要
社区问题回答（CQA）网站已成为有价值的知识存储库，个人通过询问和回答问题来交换信息。随着数量不断增加的问题和用户在社区中的高度迁移，一个关键的挑战是为向专家推荐新问题的有效策略。在本文中，我们为CQA提出了一个简单的图形扩散专家推荐模型，该模型可以胜过最先进的深度学习代表和协作模型。我们提出的方法在语义和时间信息的背景下学习了用户的专业知识，以随着时间的推移捕获其不断变化的兴趣和活动水平。来自堆栈交换网络的五个现实世界数据集的实验表明，我们的方法表现优于竞争性基线方法。此外，与最佳基线方法相比，对冷启动用户（历史记录有限）的实验表明，我们的模型平均达到了约30％的性能增长。
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.
