{"type":"ScholarlyArticle","title":"Weakly Learning to Match Experts in Online Community","authors":[{"name":"Yujie Qian","slug":"yujie-qian","given":"Yujie","family":"Qian","affiliation":"‡ Tsinghua University"},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":"Massachusetts Institute of Technology"},{"name":"Kan Wu","slug":"kan-wu","given":"Kan","family":"Wu","affiliation":"Massachusetts Institute of Technology"}],"abstract":"In online question-and-answer (QA) websites like Quora, one central issue is to find (invite) users who are able to provide answers to a given question and at the same time would be unlikely to say \"no\" to the invitation. The challenge is how to trade off the matching degree between users’ expertise and the question topic, and the likelihood of positive response from the invited users. In this paper, we formally formulate the problem and develop a weakly supervised factor graph (WeakFG) model to address the problem. The model explicitly captures expertise matching degree between questions and users. To model the likelihood that an invited user is willing to answer a specific question, we incorporate a set of correlations based on social identity theory into the WeakFG model. We use two different genres of datasets: QA-Expert and Paper-Reviewer, to validate the proposed model. Our experimental results show that the proposed model can significantly outperform (+1.5-10.7% by MAP) the state-of-the-art algorithms for matching users (experts) with community questions. We have also developed an online system to further demonstrate the advantages of the proposed method.","doi":"10.24963/ijcai.2018/534","url":"https://ijcai.org/proceedings/2018/534","pdf":"https://ijcai.org/proceedings/2018/0534.pdf","markdown":"https://ijcai.org/proceedings/2018/534.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/534","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":534,"pages":{"first":3841,"last":3847},"keywords":["Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Recommender Systems","Machine Learning: Data Mining","Natural Language Processing: NLP Applications and Tools"],"videos":[],"references":[{"position":1,"raw":"[Balog et al., 2012] Krisztian Balog, Yi Fang, Maarten de Rijke, Pavel Serdyukov, Luo Si, et al. 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