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- W2514336427 abstract "With the development of Internet, users can share knowledge by asking and answering questions on community question answering CQA websites. How to find related experts to contribute their answers is hence worthy of studying. In this paper, we propose a recommendation algorithm called collaborative expert recommendation CER for this purpose. We take full advantage of the heterogeneous information including question tags, content, answer's votes, which are considered important for identifying experts. Moreover, we combine such information by a causal assumption of questions and answers, and inner connection exploitation among different types of information such as questioner, question, answer, question and answerer, question, answer correlations, which are more explicable and reasonable comparing with the existing methods. Experiments carried out on six real-world datasets prove that CER has a better performance." @default.
- W2514336427 created "2016-09-16" @default.
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- W2514336427 date "2016-01-01" @default.
- W2514336427 modified "2023-09-26" @default.
- W2514336427 title "Collaborative Expert Recommendation for Community-Based Question Answering" @default.
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- W2514336427 doi "https://doi.org/10.1007/978-3-319-46128-1_24" @default.
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