{"type":"ScholarlyArticle","title":"EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering Services","authors":[{"name":"Jiankai Sun","slug":"jiankai-sun","given":"Jiankai","family":"Sun","affiliation":"ByteDance Inc.; Ohio State University."},{"name":"Jie Zhao","slug":"jie-zhao","given":"Jie","family":"Zhao","affiliation":"Department of Computer Science and Engineering , The Ohio State University"},{"name":"Huan Sun","slug":"huan-sun","given":"Huan","family":"Sun","affiliation":"Department of Computer Science and Engineering , The Ohio State University"},{"name":"Srinivasan Parthasarathy","slug":"srinivasan-parthasarathy","given":"Srinivasan","family":"Parthasarathy","affiliation":"Department of Computer Science and Engineering , The Ohio State University"}],"abstract":"Routing newly posted questions (a.k.a cold questions) to potential answerers with suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task. The existing methods either focus only on embedding the graph structural information and are less effective for newly posted questions, or adopt manually engineered feature vectors that are not as representative as the graph embedding methods. Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions. We conducted extensive experiments to confirm the usefulness of incorporating the textual information from question tags and demonstrate that an end-2-end framework can achieve promising performances on routing newly posted questions asked by both existing users and newly registered users.","doi":"10.24963/ijcai.2020/449","url":"https://ijcai.org/proceedings/2020/449","pdf":"https://ijcai.org/proceedings/2020/0449.pdf","markdown":"https://ijcai.org/proceedings/2020/449.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/449","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":449,"pages":{"first":3244,"last":3250},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Data Mining: Mining Text, Web, Social Media","Machine Learning: Recommender Systems","Machine Learning Applications: Applications of Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Dong et al., 2015] H. 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