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- W4306404673 abstract "Finding a qualified individual who can independently answer a question on a community question answering platform is becoming more challenging due to the increasing multidisciplinary nature of posted questions. As such, finding a group of experts to collaboratively answer the questions is of paramount importance. To this end, we propose a novel approach to form teams of experts who can collectively answer new questions. The proposed approach, called team2box, learns neural embedding representations based on the content of the posted questions, experts’ engagement with these questions, and past expert collaboration history in order to form a team to answer the posted question. It embeds experts and questions as points and existing teams as regions within the embedding space. Therefore, team2box forms a team whose members (1) collectively cover the knowledge required to answer a question, (2) have successful past experience in jointly answering similar questions, and (3) can work efficiently together to answer the question. Extensive experiments on real-life datasets from Stack Exchange show that team2box outperforms the state-of-the-art by discovering teams with on average 38.97% more covering the skills required to answer new questions and employing experts with collectively a high expertise level." @default.
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- W4306404673 date "2023-04-01" @default.
- W4306404673 modified "2023-09-30" @default.
- W4306404673 title "Embedding-based team formation for community question answering" @default.
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- W4306404673 doi "https://doi.org/10.1016/j.ins.2022.09.036" @default.
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