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User correlation model for question recommendation in community question answering

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Abstract

In this paper, we address the problem of question recommendation that automatically recommends a new question to suitable users to answer in community question answering (CQA). The major challenge of question recommendation is the accurate selection of suitable users to answer. Most of the existing approaches attempt to find suitable users in CQA by estimating user’s existing capability, user’s interest or blending both for the question. However, these methods ignore correlation among users (askers and answerers) in terms of topic preference. In this study, we propose a user correlation model (UCM) to effectively estimate degree of correlation among users in terms of topic preference. Furthermore, we present the UCM-based approach to question recommendation, which provides a mechanism to naturally integrate the correlation between answerer and asker in terms of topic preference with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that our UCM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach can increase question recommendation accuracy in CQA according to utilize the correlation between answerer and asker in terms of topic preference.

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Notes

  1. https://stackoverflow.com/

  2. https://answers.yahoo.com/

  3. https://www.quora.com/

  4. https://stackoverflow.com/questions

  5. https://stackoverflow.com/unanswered/tagged/?tab=noanswers

  6. https://www.brentozar.com/archive/2015/10/

  7. https://pypi.org/project/trueskill/

References

  1. Aditya Pal JAK (2010) Expert identification in Community Question Answering: Exploring question selection bias. ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/1871437.1871658

  2. Valdez AC et al (2016) Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives. In: Machine Learning for Health Informatics, Lecture Notes in Artificial Intelligence LNAI 9605. Heidelberg et. al. Springer pp 391–414. https://doi.org/10.1007/978-3-319-50478-0_20

  3. Cai L, Zhou G, Liu K et al (2011) Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge. In: Proceedings of the 20th ACM international conference on Information and knowledge management. pp 1321–1330. https://doi.org/10.1145/2063576.2063768

  4. Chen L, Jose JM, Yu H et al (2016) A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining: 287–296. https://doi.org/10.1145/2835776.2835809

  5. Idan Szpektor DP, Maarek Y When relevance is not enough: promoting diversity and freshness in personalized question recommendation. In: Proceedings of the 22nd international conference on World Wide Web, 2013. https://doi.org/10.1145/2488388.2488497

  6. Riahi F, Zolaktaf Z, Shafiei M, Milios E (2012) Finding expert users in community question answering. In: Proceedings of the 21st international conference companion on World Wide Web, pp 791–798. https://doi.org/10.1145/2187980.2188202

  7. Yuan Yao FXJL, Tong H (2017) Scalable algorithms for cqa post voting prediction. IEEE Trans Knowl Data Eng 29(8):1723–1736. https://doi.org/10.1109/tkde.2017.2696535

    Article  Google Scholar 

  8. Xu F, Ji Z, Wang B (2012) Dual role model for question recommendation in community question answering. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 771–780. https://doi.org/10.1145/2348283.2348387

  9. Zhou G, Lai S, Liu K, Zhao J (2012) Topic-sensitive probabilistic model for expert finding in question answer communities. ACM Int Conf Inf Knowl Manag 66(9):1662–1666. https://doi.org/10.1145/2396761.2398493

    Google Scholar 

  10. Horowitz D, Kamvar SD (2010) The anatomy of a large-scale social search engine. International Conference on World Wide Web: 431–440. https://doi.org/10.1145/1772690.1772735

  11. Zhu H, Chen E, Xiong H, Cao H, Tian J (2014) Ranking user authority with relevant knowledge categories for expert finding. World Wide Web-Internet Web Inf Syst 17(5):1081–1107. https://doi.org/10.1007/s11280-013-0217-5

    Article  Google Scholar 

  12. Guo J, Xu S, Bao S, Yu Y (2008) Tapping on the potential of q&a community by recommending answer providers. In: Proceeding of the 17th ACM conference on Information and knowledge mining, pp 921–930. https://doi.org/10.1145/1458082.1458204

  13. Liu J, Song YI, Lin CY (2011) Competition-based user expertise score estimation. In: Proceeding of the International Acm Sigir Conference on Research & Development in Information Retrieval, https://doi.org/10.1145/2009916.2009975

  14. Qiu J et al (2018) A hybrid-based method for Chinese domain lightweight ontology construction. Int J Mach Learn Cybern 9(9):1519–1531. https://doi.org/10.1007/s13042-017-0661-0

    Article  Google Scholar 

  15. Pedro JS, Karatzoglou A (2014) Question recommendation for collaborative question answering systems with rankslda. In: Proceedings of the 8th ACM Conference on Recommender systems, pp 193–200. https://doi.org/10.1145/2645710.2645736

  16. Jurczyk P, Agichtein E (2007) Discovering authorities in question answer communities by using link analysis. ACM conference on Conference on information and knowledge management: 919–922. https://doi.org/10.1145/1321440.1321575

  17. Li B, King I, Lyu MR (2011) Question routing in community question answering: Putting category in its place. ACM Conference on Information and Knowledge Management. https://doi.org/10.1145/2063576.2063885

  18. Yang L, Qiu M, Gottipati S, Zhu F, Jiang J, Sun H, Chen Z (2013) Cqarank:jointly model topics and expertise in community question answering. In: ACM International Conference on Conference on Information & Knowledge Management, pp 99–108. https://doi.org/10.1145/2505515.2505720

  19. Neshati M, Fallahnejad Z, Beigy H (2017) On dynamicity of expert finding in community question answering. Inf Process Manag 53(5):1026–1042. https://doi.org/10.1016/j.ipm.2017.04.002

    Article  Google Scholar 

  20. Qu M, Qiu G, He X, Zhang C, Wu H, Bu J, Chen C (2009) Probabilistic question recommendation for question answering communities. In: International Conference on World Wide Web, pp 1229–1230. https://doi.org/10.1145/1526709.1526942

  21. Pande V, Mukherjee T, Varma V (2013) Summarizing Answers for Community Question Answer Services. Lecture Notes in Computer Science: 151–161. https://doi.org/10.1007/978-3-642-40722-2_16

  22. Nie L, Davison BD, Qi X (2006) Topical link analysis for web search. International Acm Sigir Conference on Research & Development in Information Retrieval: 91–98, https://doi.org/10.1145/1148170.1148189

  23. Nie L, Davison BD, Wu B (2007) From whence does your authority come? utilizing community relevance in ranking. National Conference on Artificial Intelligence: 1421–1426. https://doi.org/10.1145/1277741.1277952

  24. Wang Q, Liu J, Wang B, Guo L (2014) A regularized competition model for question difficulty estimation in community question answering services. In: Conference on Empirical Methods in Natural Language Processing: 1115–1126. https://doi.org/10.3115/v1/d14-1118

  25. Haveliwala TH (2002) Topic-sensitive pagerank. In: Proceedings of the eleventh international conference on World Wide Web, pp 517–526. https://doi.org/10.1145/511446.511513

  26. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101:5228–5235. https://doi.org/10.1073/pnas.0307752101

    Article  Google Scholar 

  27. Toba H, Ming Z, Adriani M, Chua T (2014) Discovering high quality answers in community question answering archives using a hierarchy of classifiers. Inf Sci 261(5):101–115. https://doi.org/10.1016/j.ins.2013.10.030

    Article  MathSciNet  Google Scholar 

  28. Tsur G, Pinter Y, Szpektor I et al (2016) Identifying web queries with question intent. International Conference on World Wide Web: 783–793. https://doi.org/10.1145/2872427.2883058

  29. Zhang WE, Sheng QZ, Lau JH, Abebe E (2017) Detecting duplicate posts in programming qa communities via latent semantics and association rules. In: International Conference on World Wide Web, pp 1221–1229. https://doi.org/10.1145/3038912.3052701

  30. Wang X, Huang C, Yao L, Benatallah B, Dong M (2018) A survey on expert recommendation in community question answering. J Comput Sci Technol 33(4):625–653. https://doi.org/10.1007/s11390-018-1845-0

    Article  Google Scholar 

  31. Weng J, Lim E, Jiang J et al (2010) TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on Web search and data mining, pp 261–270. https://doi.org/10.1145/1718487.1718520

  32. Ni X, Lu Y, Quan X, Liu W, Hua B (2012) User interest modeling and its application for question recommendation in user-interactive question answering systems. Inf Process Manag 48(2):218–233. https://doi.org/10.1016/j.ipm.2011.09.002

    Article  Google Scholar 

  33. Wei-Nan Zhang YZTLT-SC, Ming Z-Y (2016) Capturing the semantics of key phrases using multiple languages for question retrieval. IEEE Trans Knowl Data Eng 28(4):888–900. https://doi.org/10.1109/tkde.2015.2502944

    Article  Google Scholar 

  34. Chen Z, Zhang C, Zhao Z, Yao C, Cai D (2018) Question retrieval for community-based question answering via heterogeneous social influential network. Neurocomputing 285:117–124. https://doi.org/10.1016/j.neucom.2018.01.034

    Article  Google Scholar 

  35. Zhou G, Huang JX (2017) Modeling and Learning Distributed Word Representation with Metadata for Question Retrieval. IEEE Trans Knowl Data Eng 29(6):1226–1239. https://doi.org/10.1109/tkde.2017.2665625

    Article  Google Scholar 

  36. Zhou X, Hu B, Chen Q et al (2017) Recurrent convolutional neural network for answer selection in community question answering. Neurocomputing: 8–18. https://doi.org/10.1016/j.neucom.2016.07.082

  37. Zhao Z, Wei F, Zhou M (2015) Cold-Start Expert Finding in Community Question Answering via Graph Regularization, International Conference on Database Systems for Advanced Applications. https://doi.org/10.1007/978-3-319-18120-2_2

  38. Zhou Y, Cong G, Cui B (2009) Routing questions to the right users in online communities. IEEE International Conference on Data Engineering: 919–922. https://doi.org/10.1109/ICDE.2009.44

  39. Zhao Z, Yang Q, Cai D, He X, Zhuang Y (2016) Expert finding for community-based question answering via ranking metric network learning. In: International Joint Conference on Artificial Intelligence, pp 3000–3006. https://doi.org/10.1109/tkde.2014.2356461

  40. Zhou Z, Zhang L, He X, Ng W (2015) Expert finding for question answering via graph regularized matrix completion. IEEE Trans Knowl Data Eng 27(4):993–1004. https://doi.org/10.1109/TKDE.2014.2356461

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China

    Chaogang Fu

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Fu, C. User correlation model for question recommendation in community question answering. Appl Intell 50, 634–645 (2020). https://doi.org/10.1007/s10489-019-01544-y

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