Cold-Start Expert Finding in Community Question Answering via Graph Regularization

详细信息    查看全文

• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9049
• 期：1
• 页码：21-38
• 全文大小：442 KB
• 参考文献：1. Blei, DM, Ng, AY, Jordan, MI (2003) Latent dirichlet allocation. The Journal of machine Learning research 3: pp. 993-1022
  2. Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In: SIGKDD, pp. 866-74. ACM (2008)
  3. Deng, H., King, I., Lyu, M.R.: Formal models for expert finding on dblp bibliography data. In: ICDM, pp. 163-72. IEEE (2008)
  4. Dror, G., Koren, Y., Maarek, Y., Szpektor, I.: I want to answer; who has a question?: yahoo! answers recommender system. In: Proceedings of SIGKDD, pp. 1109-117. ACM (2011)
  5. Figueroa, A., Neumann, G.: Learning to rank effective paraphrases from query logs for community question answering. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)
  6. Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 575-90. ACM (2012)
  7. GSL. https://www.gnu.org/software/gsl/
  8. Guo, J., Xu, S., Bao, S., Yu, Y.: Tapping on the potential of q&a community by recommending answer providers. In: CIKM, pp. 921-30. ACM (2008)
  9. Hashemi, S.H., Neshati, M., Beigy, H.: Expertise retrieval in bibliographic network: a topic dominance learning approach. In: CIKM, pp. 1117-126. ACM (2013)
  10. Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR, pp. 50-7. ACM (1999)
  11. Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: CIKM, pp. 919-22. ACM (2007)
  12. Kleinberg, JM (1999) Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46: pp. 604-632 CrossRef
  13. Li, W.-J., Yeung, D.-Y.: Relation regularized matrix factorization. In: IJCAI, pp. 1126-131 (2009)
  14. Lin, J., Sugiyama, K., Kan, M.-Y., Chua, T.-S.: Addressing cold-start in app recommendation: Latent user models constructed from twitter followers (2013)
  15. Liu, J., Song, Y.-I., Lin, C.-Y.: Competition-based user expertise score estimation. In: SIGIR, pp. 425-34. ACM (2011)
  16. Liu, X., Croft, W.B., Koll, M.: Finding experts in community-based question-answering services. In: CIKM, pp. 315-16. ACM (2005)
  17. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287-96. ACM (2011)
  18. Miao, G., Moser, L.E., Yan, X., Tao, S., Chen, Y., Anerousis, N.: Generative models for ticket resolution in expert networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 733-42. ACM (2010)
  19. Mimno, D., McCallum, A.: Expertise modeling for matching papers with reviewers. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 500-09. ACM (2007)
  20. Park, S.-T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: RecSys, pp. 21-8. ACM (2009)
  21. Purushotham, S., Liu, Y., Kuo, C.-C.J.: Collaborative topic regression with social matrix factorization for recommendation systems (2012). arXiv preprint arXiv:1206.4684
  22. Riahi, F., Zolaktaf, Z., Shafiei, M., Milios, E.: Finding expert users in community question answering. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 791-98. ACM (2012)
  23. Srivastava, A.N., Sahami, M.: Text mining: Classification, clustering, and applications. CRC Press (2009)
  24. Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448-56. ACM (2011)
  25. Wang, G., Gill, K., Mohanlal, M., Zheng, H., Zhao, B.Y.: Wisdom in the social crowd: an analysis of quora. In: Proceedings of WWW, pp. 1341-352. International World Wide Web Conferences Steering Committee (2013)
  26. Wang, H., Chen, B., Li, W.-J.: Collaborative topic regression with social regularization for tag recommendation. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2719-725. AAAI Press (2013)
  27. Xu, F., Ji, Z., Wang, B.: Dual role model for question recommendation in community question answering. In: Proceedings of SIGIR, pp. 771-80. ACM (2012)
  28. Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., Chen, Z.: Cqarank: jointly model topics and expertise in community question answering. In: CIKM, pp. 99-08. ACM (2013)
  29. Yin, H, Cui, B, Li, J, Yao, J, Chen, C (2012) Challenging the
• 作者单位：Database Systems for Advanced Applications
• 丛书名：978-3-319-18119-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Expert finding for question answering is a challenging problem in Community-based Question Answering (CQA) systems such as Quora. The success of expert finding is important to many real applications such as question routing and identification of best answers. Currently, many approaches of expert findings rely heavily on the past question-answering activities of the users in order to build user models. However, the past question-answering activities of most users in real CQA systems are rather limited. We call the users who have only answered a small number of questions the cold-start users. Using the existing approaches, we find that it is difficult to address the cold-start issue in finding the experts. In this paper, we formulate a new problem of cold-start expert finding in CQA systems. We first utilize the “following relations" between the users and topical interests to build the user-to-user graph in CQA systems. Next, we propose the Graph Regularized Latent Model (GRLM) to infer the expertise of users based on both past question-answering activities and an inferred user-to-user graph. We then devise an iterative variational method for inferring the GRLM model. We evaluate our method on a well-known question-answering system called Quora. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art expert finding algorithms.