Study on Finding Experts in Community Question-Answering System

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As one of the new service model of Web 2.0, the emergence of Community Question-Answering system brings a new way for users to obtain information. However, the explosive growth of users and information, it will be hard for users to obtain the information quickly and accurately. Therefore, it is important to find experts in Community Question-Answering system to improve the accuracy and efficiency of information obtaining. This paper firstly analyzed the relationship among users, questions, and answers in Community Question-Answering system, and built the user diagram, and then by means of the Web mining technology, that is the link analysis weighted HITS algorithm, to find experts out. Finally, three evaluation indices were used to measure the validity of the experts finding algorithm. Experimental results show the effectiveness of the weighted HITS algorithm.

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1760-1764

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Weixi Kong, Yiqun Liu, Min Zhang, et al. Answer Quality Analysis on Community Question Answering. Journal of Chinese Information Processing, 2011, 25(1), pp.3-8.

Google Scholar

[2] Kantardzic M. Data mining: concepts, models, methods, and algorithms [M]. John Wiley & Sons, 2011, pp.300-326.

Google Scholar

[3] X Liu, Croft W B, Koll M. Finding experts in community-based question-answering services. Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, 2005, pp.315-316.

DOI: 10.1145/1099554.1099644

Google Scholar

[4] Y Zhou, G Cong, B Cui, et al. Routing questions to the right users in online communities. Data Engineering, 2009. ICDE'09. IEEE 25th International Conference on. IEEE, 2009, pp.700-711.

DOI: 10.1109/icde.2009.44

Google Scholar

[5] J Bian, Y Liu, D Zhou, et al. Learning to recognize reliable users and content in social media with coupled mutual reinforcement. Proceedings of the 18th international conference on World Wide Web. ACM, 2009, pp.51-60.

DOI: 10.1145/1526709.1526717

Google Scholar

[6] Jurczyk P, Agichtein E. Hits on question answer portals: exploration of link analysis for author ranking. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2007, pp.845-846.

DOI: 10.1145/1277741.1277938

Google Scholar

[7] Jurczyk P, Agichtein E. Hits on question answer portals: exploration of link analysis for author ranking. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2007, pp.845-846.

DOI: 10.1145/1277741.1277938

Google Scholar

[8] Kleinberg,J. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 1999, 46(5), pp.604-632.

DOI: 10.1145/324133.324140

Google Scholar