Abstract
Community Question Answering services (CQAs) have become ubiquitous, and are widely used. Hence, it would be beneficial if we can mine useful inferences from these data sets to improve these services. For example, if we can infer or identify expertise of users’ from these data sets, we can route questions to the right people. With the identification of expertise, number of experts needed to cover a set of topics (in a CQA service) can also be optimized. This paper addresses the problem of inferring expertise.
Current approaches infer expertise using traditional link-based methods such as PageRank or HITS, and others (e.g., number of answers given by a user or Z_score). Although an ask-answer graph can be generated for a CQA data set based on the ask-answer paradigm (who answers whose questions), this graph is different, in its semantics, from the web graphs. We posit that both graph structure and domain information related to an answerer (e.g., answer quality) is critical for inferring the expertise of users. Based on this observation, we propose the ExpertRank framework to compute users’ expertise. We establish that the information used has a bearing on the accuracy of results. We present our algorithm along with extensive experimental analysis that indicates superiority of our approach as compared to other link-based methods.
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Cai, Y., Chakravarthy, S. (2013). Expertise Ranking of Users in QA Community. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37487-6_5
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DOI: https://doi.org/10.1007/978-3-642-37487-6_5
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