Abstract
Expert finding is important to the development of community question answering websites and e-learning. In this study, we propose a topic-sensitive probabilistic model to estimate the user authority ranking for each question, which is based on the link analysis technique and topical similarities between users and questions. Most of the existing approaches focus on the user relationship only. Compared to the existing approaches, our method is more effective because we consider the link structure and the topical similarity simultaneously. We use the real-world data set from Zhihu (a famous CQA website in China) to conduct experiments. Experimental results show that our algorithm outperforms other algorithms in the user authority ranking.
The research work described in this article has been substantially supported by “the Fundamental Research Funds for the Central Universities” (Project Number: 46000-31610009).
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References
Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: Proceedings of the International Conference on Web Search and Web Data Mining (WSDM), pp. 183–194 (2008)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question- answering forums: the case of Yahoo! answers. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 866–874 (2008)
Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the 16th ACM Conference on Information and Knowledge Management (CIKM), pp. 919–922 (2007)
Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web (WWW), pp. 221–230 (2007)
Guo, J., Xu, S., Bao, S., Yu, Y.: Tapping on the potential of q & a community by recommending answer providers. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM), pp. 921–930 (2008)
Liu, X., Croft, W.B., Koll, M.: Finding experts in community-based question answering services. In: Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management (CIKM), pp. 315–316 (2005)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the 3rd International Conference on Web Search and Web Data Mining (WSDM), pp. 261–270 (2010)
Zhou, G., Lai, S., Liu, K., Zhao, J.: Topic-sensitive probabilistic model for expert finding in question answer communities. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM), pp. 1662–1666 (2012)
Haveliwala, T.H.: Topic-sensitive PageRank. In: Proceedings of the 11th International World Wide Web Conference (WWW), pp. 517–526 (2002)
Zhao, T., Bian, N., Li, C., Li, M.: Topic-level expert modeling in community question answering. In: Proceedings of the 13th SIAM International Conference on Data Mining (SDM), pp. 776–784 (2013)
Chen, B.-C., Guo, J., Tseng, B., Yang, J.: User reputation in a comment rating environment. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 159–167 (2011)
Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining (WSDM), pp. 45–54 (2011)
Liu, Y., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., Chen, Z.: CQARank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM), pp. 99–108 (2013)
Rao, Y.H., Li, Q., Wenyin, L., Wu, Q.Y., Quan, X.J.: Affective topic model for social emotion detection. Neural Netw. 58, 29–37 (2014)
Rao, Y.H., Li, Q., Mao, X.D., Wenyin, L.: Sentiment topic models for social emotion mining. Inf. Sci. 266, 90–100 (2014)
Rao, Y.H., Lei, J.S., Wenyin, L., Li, Q., Chen, M.L.: Building emotional dictionary for sentiment analysis of online news. World Wide Web J. 17, 723–742 (2014)
Rao, Y.H., Li, Q.: Term weighting schemes for emerging event detection. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 105–112 (2012)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)
Norris, J.R.: Markov Chains. Cambridge University Press, Cambridge (1998)
Xie, H.R., Li, Q., Mao, X.D., Li, X.D., Cai, Y., Rao, Y.H.: Community-aware user profile enrichment in folksonomy. Neural Netw. 58, 111–121 (2014)
Li, Q., Lau, R.W.H., Wah, B., Ashman, H., Leung, E., Li, F., Lee, V.: Guest editors’ introduction: emerging internet technologies for e-learning. IEEE Internet Comput. 13(4), 11–17 (2009)
Zou, D., Xie, H., Li, Q., Wang, F.L., Chen, W.: The load-based learner profile for incidental word learning task generation. In: Popescu, E., Lau, R.W.H., Pata, K., Leung, H., Laanpere, M. (eds.) ICWL 2014. LNCS, vol. 8613, pp. 190–200. Springer, Heidelberg (2014)
Acknowledgements
The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper. The research described in this paper has been supported by the National Natural Science Foundation of China (Grant No. 61502545), and “the Fundamental Research Funds for the Central Universities” (Grant No. 46000-31121401).
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Liu, X., Ye, S., Li, X., Luo, Y., Rao, Y. (2015). ZhihuRank: A Topic-Sensitive Expert Finding Algorithm in Community Question Answering Websites. In: Li, F., Klamma, R., Laanpere, M., Zhang, J., Manjón, B., Lau, R. (eds) Advances in Web-Based Learning -- ICWL 2015. ICWL 2015. Lecture Notes in Computer Science(), vol 9412. Springer, Cham. https://doi.org/10.1007/978-3-319-25515-6_15
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