Abstract
Online Communities for Question Answering (CQA) such as Quora and Stack Overflow face the challenge of providing high quality answers to the questions asked by their users. Although CQA frameworks receive new questions in a linear rate, the rate of the unanswered questions increases in an exponential way. This variation eventually compromise effectiveness of the CQA frameworks as knowledge sharing platforms. The main cause for this challenge is the improper routing of questions to the potential answerers, field experts or interested users. The proposed technique QR-DSSM uses deep semantic similarity model (DSSM) to extract semantic similarity features using deep neural networks. The extracted semantic features are used to rank the profiles of the answerers by their relevance the routed question. QR-DSSM maps the asked questions and the profiles of the users into a latent semantic space where the relevance is measured using cosine similarity between the two; questions and users’ profiles. QR-DSSM achieved MRR score of 0.1737. QR-DSSM outperformed the baseline models such as query likelihood language model (QLLM), Latent Dirichlet Allocation (LDA), SVM classification technique and RankingSVM learning to rank technique.
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References
Balog, K., Azzopardi, L., de Rijke, M.: A language modeling framework for expert finding. Inf. Process. Manage. 45(1), 1–19 (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2001)
Deng, L., Yu, D.: Deep learning: methods and applications. Technical report MSR-TR-2014-21
Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015, pp. 278–288 (2015)
Gao, J., He, X., Nie, J.Y.: Clickthrough-based translation models for web search: from word models to phrase models. In: CIKM
Gao, J., Pantel, P., Gamon, M., He, X., Deng, L.: Modeling interestingness with deep neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2–13 (2014)
Herbrich, R.: Learning Kernel Classifiers: Theory and Algorithms
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM 2013, pp. 2333–2338 (2013)
Ji, Z., Wang, B.: Learning to rank for question routing in community question answering. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM 2013, pp. 2363–2368 (2013)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Li, B., King, I.: Routing questions to appropriate answerers in community question answering services. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1585–1588 (2010)
Liu, D.R., Chen, Y.H., Kao, W.C., Wang, H.W.: Integrating expert profile, reputation and link analysis for expert finding in question-answering websites. Inf. Process. Manage. 49(1), 312–329 (2013)
van den Oord, A., Dieleman, S., Schrauwen, B.: Deep content-based music recommendation. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, pp. 2643–2651 (2013)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report 1999-66, Stanford InfoLab, previous number = SIDL-WP-1999-0120
Qu, M., Qiu, G., He, X., Zhang, C., Wu, H., Bu, J., Chen, C.: Probabilistic question recommendation for question answering communities. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 1229–1230 (2009)
Riahi, F., Zolaktaf, Z., Shafiei, M., Milios, E.: Finding expert users in community question answering. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012 Companion, pp. 791–798 (2012)
Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approx. Reasoning 50(7), 969–978 (2009)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)
Yan, Z., Zhou, J.: Optimal answerer ranking for new questions in community question answering. Inf. Process. Manage. 51(1), 163–178 (2015)
Ye, X., Li, J., Qi, Z., He, X.: Enhancing retrieval and ranking performance for media search engine by deep learning. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 1174–1180 (2016)
Zhao, Z., Yang, Q., Cai, D., He, X., Zhuang, Y.: Expert finding for community-based question answering via ranking metric network learning. In: IJCAI
Zhou, Y., Cong, G., Cui, B., Jensen, C.S., Yao, J.: Routing questions to the right users in online communities. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, ICDE 2009, pp. 700–711 (2009)
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Azzam, A., Tazi, N., Hossny, A. (2017). A Question Routing Technique Using Deep Neural Network for Communities of Question Answering. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_3
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