Yuan, Sha, Zhang, Yu, Tang, Jie, Hall, Wendy and Bautista Cabota, Juan (2019) Expert finding in community question answering: a review. Artificial Intelligence Review, 1-32. (doi:10.1007/s10462-018-09680-6).
Abstract
The rapid development of Community Question Answering (CQA) satisfies users’ quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. The new features of CQA (such as huge volume, sparse data and crowdsourcing) violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify the recent solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the crowdsourcing situation. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boost the performance. Further, we compare the performance of different models on different types of matching tasks, including textvs.text, graphvs.text, audiovs.text and videovs.text. The results will help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA.
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- Faculties (pre 2018 reorg) > Faculty of Engineering and the Environment (pre 2018 reorg) > Southampton Marine & Maritime Institute (pre 2018 reorg)
- Faculties (pre 2018 reorg) > Faculty of Natural and Environmental Sciences (pre 2018 reorg) > Institute for Life Sciences (pre 2018 reorg)
Current Faculties > Faculty of Environmental and Life Sciences > Institute for Life Sciences > Institute for Life Sciences (pre 2018 reorg)
Institute for Life Sciences > Institute for Life Sciences (pre 2018 reorg) - Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science > Web and Internet Science
School of Electronics and Computer Science > Web and Internet Science
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