Advertisement

Expert2Vec: Experts Representation in Community Question Answering for Question Routing

  • Sara MumtazEmail author
  • Carlos Rodriguez
  • Boualem Benatallah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

Communities of Question Answering (CQAs) are rapidly growing communities for exchanging information in the form of questions and answers. They rely on the contributions of users (i.e., members of the community) who have appropriate domain knowledge and can provide helpful answers. In order to deliver the most appropriate and valuable answers, identification of such users (experts) is critically important. However, a common problem faced in CQAs is that of poor expertise matching, i.e., routing of questions to inappropriate users. In this paper, we focus on Stack Overflow (a programming CQA) and address this problem by proposing an embedding based approach that integrates users’ textual content obtained from the community (e.g., answers) and community feedback in a unified framework. Our embedding-based approach is used to find the best relevant users for a given question by computing the similarity between questions and our user expertise representation. Then, our framework exploits feedback from the community to rank the relevant users according to their expertise. We experimentally evaluate the performance of the proposed approach using Stack Overflow’s dataset, compare it with state-of-the-art models and demonstrate that it can produce better results than the alternative models.

Keywords

Experts finding Question and answering communities Embeddings models Stack overflow Expert representation 

Notes

Acknowledgement

The work of the second and third authors is supported by Data to Decisions CRC (D2D-CRC).

References

  1. 1.
    Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Discovering value from community activity on focused question answering sites: a case study of stack overflow. In: Proceedings of KDD 2012, pp. 850–858 (2012)Google Scholar
  2. 2.
    Baeza-Yates, R., Ribeiro, B.: Modern Information Retrieval (2011)Google Scholar
  3. 3.
    Balog, K., Bogers, T., Azzopardi, L., de Rijke, M., van den Bosch, A.: Broad expertise retrieval in sparse data environments. In: Proceedings of SIGIR 2007, pp. 551–558 (2007)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of Yahoo! answers. In: Proceedings of SIGKDD 2008, pp. 866–874 (2008)Google Scholar
  6. 6.
    Christopher, D.M., Prabhakar, R., Hinrich, S.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008). An Introduction to Information Retrieval 151(177), 5zbMATHGoogle Scholar
  7. 7.
    Dalip, D.H., Gonçalves, M.A., Cristo, M., Calado, P.: Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow. In: Proceedings of SIGIR 2013, pp. 543–552 (2013)Google Scholar
  8. 8.
    De Boom, C., Van Canneyt, S., Demeester, T., Dhoedt, B.: Representation learning for very short texts using weighted word embedding aggregation. Pattern Recogn. Lett. 80, 150–156 (2016)CrossRefGoogle Scholar
  9. 9.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)CrossRefGoogle Scholar
  10. 10.
    van Dijk, D., Tsagkias, M., de Rijke, M.: Early detection of topical expertise in community question answering. In: Proceedings of SIGIR 2015, pp. 995–998 (2015)Google Scholar
  11. 11.
    Gui, H., Zhu, Q., Liu, L., Zhang, A., Han, J.: Expert finding in heterogeneous bibliographic networks with locally-trained embeddings. CoRR abs/1803.03370 (2018)Google Scholar
  12. 12.
    Ha-Thuc, V., Venkataraman, G., Rodriguez, M., Sinha, S., Sundaram, S., Guo, L.: Personalized expertise search at LinkedIn. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 1238–1247. IEEE (2015)Google Scholar
  13. 13.
    Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: ICML 2015, pp. 957–966 (2015)Google Scholar
  14. 14.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML 2014, pp. 1188–1196 (2014)Google Scholar
  15. 15.
    Liu, M., Liu, Y., Yang, Q.: Predicting best answerers for new questions in community question answering. In: International Conference on Web-Age Information Management, pp. 127–138 (2010)CrossRefGoogle Scholar
  16. 16.
    Liu, X., Croft, W.B., Koll, M.: Finding experts in community-based question-answering services. In: CIKM 2005, pp. 315–316 (2005)Google Scholar
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  18. 18.
    Mumtaz, S., Wang, X.: Identifying Top-K influential nodes in networks. In: CIKM 2017, pp. 2219–2222 (2017)Google Scholar
  19. 19.
    Qian, Y., Tang, J., Wu, K.: Weakly learning to match experts in online community. In: IJCAI 2018, pp. 3841–3847 (2018)Google Scholar
  20. 20.
    Quan, X., Kit, C., Ge, Y., Pan, S.J.: Short and sparse text topic modeling via self-aggregation. In: IJCAI 2015 (2015)Google Scholar
  21. 21.
    Yang, L., et al.: CQARank: Jointly model topics and expertise in community question answering. In: Proceedings of CIKM 2013, pp. 99–108 (2013)Google Scholar
  22. 22.
    Yimam-Seid, D., Kobsa, A.: Expert-finding systems for organizations: Problem and domain analysis and the DEMOIR approach. J. Organ. Comput. Electron. Commer. 13(1), 1–24 (2003)CrossRefGoogle Scholar
  23. 23.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: WWW 2007, pp. 221–230 (2007)Google Scholar
  24. 24.
    Zhao, Z., Lu, H., Zheng, V.W., Cai, D., He, X., Zhuang, Y.: Community-based question answering via asymmetric multi-faceted ranking network learning. In: AAAI 2017, pp. 3532–3539 (2017)Google Scholar
  25. 25.
    Zhao, Z., Yang, Q., Cai, D., He, X., Zhuang, Y.: Expert finding for community-based question answering via ranking metric network learning. In: IJCAI 2016, pp. 3000–3006 (2016)Google Scholar
  26. 26.
    Zheng, C., Zhai, S., Zhang, Z.: A deep learning approach for expert identification in question answering communities. CoRR abs/1711.05350 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sara Mumtaz
    • 1
    Email author
  • Carlos Rodriguez
    • 1
  • Boualem Benatallah
    • 1
  1. 1.School of Computer Science and EngineeringUNSW SydneySydneyAustralia

Personalised recommendations