Abstract
At present, people no longer meet the way of communication between users and the Internet. And more and more people choose the interaction between users and users to get information. The community question answering system is one of the new information sharing model. In the community question answering system, users are not only the questioner but also the answer and the question is the link between the users. With the increasing number of users and the increasing number of questions and answers, it makes many questions which just were raised disappear in the category pages of the home page. Leading to the efficiency of the questions be answered greatly reduce. Aim at the recommended user’s interest, ability and time. In this paper we construct a dynamic user interest model and user expertise model. Experimental results show that the recommendation mechanism improves the efficiency of the recommendation to a certain extent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Z.F., Li, Q.D.: Review of community question answering system. Comput. Sci. 37(11), 19–23 (2011)
Liu, X.Y., Bruce Croft, W., et al.: Finding experts in community-based question-answering services. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 315–316 (2005)
Zhang, J., Tang, J., Li, J.: Expert finding in a social network. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 1066–1069. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71703-4_106
Wu, H., Wang, Y., Cheng, X.: Incremental probabilistic latent semantic analysis for automatic question recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 99–106 (2008)
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, pp. 921–930 (2008)
Yu, S.H.: Research on key technologies of personalized social network based on recommendation system. Nat. Def. Sci. Technol. Univ. 24–40 (2011)
Jurczyk, P., Agichtein, E.: Hits on question answer portals: exploration of link analysis for author ranking. In: Proceedings of 30th Annual International ACM SIGIR Conference, pp. 845–846 (2007)
Urczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of ACM 17th Conference on Information and Knowledge Management, pp. 919–922 (2007)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Stanford Digital Library Working Paper SIDL-WP-1999-0120
Kleinberg, J.: Authoritative sources in a hyper linked environment (1998)
Duan, W.C., Hu, P.: An improved PageRank algorithm based on topic feature and time factor. Comput. Eng. Des. 31(4), 866–868 (2010)
Yang, J.S., Ling, P.L.: Improvement of PageRank algorithm for search engine. Comput. Proj. 35(22), 35–37 (2009)
Deng, D.J., Zhou, C.L.: Improved PageRank algorithm based on content correlation and time analysis. Comput. Digit. Eng. 39(1), 25–27 (2011)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61365010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, J., Su, L., Chen, J., Jiang, D. (2018). Question Recommendation Based on User Model in CQA. In: Wan, J., et al. Cloud Computing, Security, Privacy in New Computing Environments. CloudComp SPNCE 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-319-69605-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-69605-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69604-1
Online ISBN: 978-3-319-69605-8
eBook Packages: Computer ScienceComputer Science (R0)