近日，博士生钱凌飞的研究成果被ELSEVIER旗下计算机top期刊Knowledge Based System（KBS）录用，KBS为中科院一区期刊，CCF推荐期刊C类。

  标题：Heterogeneous information network embedding based on multiperspective metapath for question routing（基于多视角异质图嵌入的问题路由）

  摘要：Question routing is an important task on community question answering websites. Network embedding methods have achieved great success in question routing. However, most network embedding methods focus on the content of the questions and the answering history of the answerers. Answers that are similar to the accepted answer are treated in the same way as bad answers only because they are not "accepted". As a result, the profiles of users are not fully assessed in these methods. To solve these problems, we propose a multiperspective metapath-based representation learning network for question routing, namely, MPQR. (1) MPQR learns multiperspective representations of question answerers, question raisers and questions based on a heterogeneous information network (HIN) that is constructed from answering records and voting information. Interest and expertise representations of users are learned at the same time. (2) A scoring function outputs the probability of each answerer providing the best answer. Pointwise loss and pairwise loss are combined to rank the answerers. The pointwise loss helps MPQR give more attention to potential answers with higher numbers of votes than bad answers. Experiments on real-world datasets show that MPQR outperforms state-of-the-art network embedding methods that ignore voting information.

  问题路由是社区问答系统中的一项重要的任务。网络嵌入的方法目前在问题路由任务中取得了巨大的成功。然而，大多数网络嵌入方法的用户表征构建都过于单一。此外，一些优质但没被接收的回答往往缺乏关注。为了解决这些问题，我们提出一种基于多视角的网络嵌入方法，从用户的回答兴趣和回答专业度两个角度学习用户表征。此外，我们在对专家进行排序的时候，结合了pointwise和pairwise的方式，使得模型不仅可以考虑到被接受的答案专家，还能更加注意到那些其他优质的专家。