摘要(英) |
With the ever-changing technology, we humans have to be willing to keep on learning in order to avoid being demoted by the world. Therefore, the reasons above led to the rise of the community question answering websites, such as Stack Overflow, Yahoo Answers, Quora, Zhihu (知乎), and so on and so forth. Users can ask questions, answer questions, exchange and discuss ideas with each other in the above platform.
Although the rise of community question answering websites can surely bring great convenience to users, there is still room for improvement. Due to the large numbers of questions, most questions usually receive no response or get inappropriate answers. It is without doubt to rely on luck and time to get correct answers in time. Therefore, we believe that if we can find experts precisely in CQA websites, we can improve the efficiency of the participation rate by routing right questions to experts.
In this study, we firstly utilize TEM (Topic Expertise Model), which is an unsupervised model published by Yang, Liu, et al. (2013), for capturing the degree of expertise of question and answerer under different topic. Furthermore, we utilize History Post Embedding, which is published in this thesis by using word embedding techniques, to extract semantic meanings to enhance the understanding of question sets. Finally, we combine the vector of topical expertise with History Post Embedding and perform a recommendation formula to rank experts. We target Stack Overflow, which is one of the biggest computer programming field CQA websites in the world, as our research goal and obtain good result. Moreover, we expect the research result to be available on other CQA websites.
The main contribution of this thesis is combining TEM model with distributed representation of user historical information which can solve the problem of low participation rate in CQA websites when social network structure is not so complete.
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參考文獻 |
1. Riahi, F., et al. Finding expert users in community question answering. in Proceedings of the 21st International Conference on World Wide Web. 2012. ACM.
2. Guo, J., et al. Tapping on the potential of q&a community by recommending answer providers. in Proceedings of the 17th ACM conference on Information and knowledge management. 2008. ACM.
3. "Stackoverflow.com Site Info". Alexa Internet.: p. Retrieved 2017-08-14.
4. Spolsky, J., "Stack Overflow Launches". Joel on Software. (2008-09-15).
5. Duan, J., J. Zeng, and B. Luo. Identification of opinion leaders based on user clustering and sentiment analysis. in Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 01. 2014. IEEE Computer Society.
6. Weng, J., et al. Twitterrank: finding topic-sensitive influential twitterers. in Proceedings of the third ACM international conference on Web search and data mining. 2010. ACM.
7. Agarwal, N., et al. Identifying the influential bloggers in a community. in Proceedings of the 2008 international conference on web search and data mining. 2008. ACM.
8. Yu, X., X. Wei, and X. Lin, Algorithms of BBS Opinion Leader Mining Based on Sentiment Analysis. WISM, 2010. 10: p. 360-369.
9. Katz, E. and P.F. Lazarsfeld, Personal Influence, The part played by people in the flow of mass communications. 1966: Transaction Publishers.
10. Wang, W. and W.N. Street, Modeling influence diffusion to uncover influence centrality and community structure in social networks. Social Network Analysis and Mining, 2015. 5(1): p. 15.
11. Bonacich, P., Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 1972. 2(1): p. 113-120.
12. Katz, L., A new status index derived from sociometric analysis. Psychometrika, 1953. 18(1): p. 39-43.
13. Page, L., et al., The PageRank citation ranking: Bringing order to the web. 1999, Stanford InfoLab.
14. Zhu, H., et al., Ranking user authority with relevant knowledge categories for expert finding. World Wide Web, 2014. 17(5): p. 1081-1107.
15. Zhou, G., et al. Topic-sensitive probabilistic model for expert finding in question answer communities. in Proceedings of the 21st ACM international conference on Information and knowledge management. 2012. ACM.
16. Liu, X., W.B. Croft, and M. Koll. Finding experts in community-based question-answering services. in Proceedings of the 14th ACM international conference on Information and knowledge management. 2005. ACM.
17. Miller, D.R., T. Leek, and R.M. Schwartz. A hidden Markov model information retrieval system. in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. 1999. ACM.
18. Lavrenko, V. and W.B. Croft. Relevance based language models. in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. 2001. ACM.
19. Xu, J. and W.B. Croft. Cluster-based language models for distributed retrieval. in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. 1999. ACM.
20. Ponte, J.M. and W.B. Croft. A language modeling approach to information retrieval. in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 1998. ACM.
21. Qu, M., et al. Probabilistic question recommendation for question answering communities. in Proceedings of the 18th international conference on World wide web. 2009. ACM.
22. Yang, L., et al. Cqarank: jointly model topics and expertise in community question answering. in Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2013. ACM.
23. Blei, D.M., A.Y. Ng, and M.I. Jordan, Latent dirichlet allocation. Journal of machine Learning research, 2003. 3(Jan): p. 993-1022.
24. Mikolov, T., et al., Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
25. Rong, X., word2vec parameter learning explained. arXiv preprint arXiv:1411.2738, 2014.
26. Adamic, L.A., et al. Knowledge sharing and yahoo answers: everyone knows something. in Proceedings of the 17th international conference on World Wide Web. 2008. ACM.
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