کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11007995 1840489 2018 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Exploiting cross-source knowledge for warming up community question answering services
ترجمه فارسی عنوان
بهره برداری از دانش متقابل برای گرم کردن پرسش های اجتماعی پاسخ دادن به خدمات
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Community Question Answering (CQA) services such as Yahoo! Answers, Quora and StackOverflow are collaborative platforms where users can share and exchange their knowledge explicitly by asking and answering questions. One essential task in CQA is learning topical expertise of users, which may benefit many applications such as question routing and best answers identification. One limitation of existing related works is that they only consider the warm-start users who have posted many questions or answers, while ignoring cold-start users who have few posts. In this paper, we aim to exploit knowledge from cross sources such as GitHub and StackOverflow to build up the richer views of expertise for better CQA. Inspired by the idea of Bayesian co-training, we propose a topical expertise model from the perspective of multi-view learning. Specifically, we incorporate the consistency existing among multiple views into a unified probabilistic graphic model. Comprehensive experiments on two real-world datasets demonstrate the performance of our proposed model with the comparison of some state-of-the-art ones.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 320, 3 December 2018, Pages 25-34
نویسندگان
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