Skip to main content
SpringerLink
Log in

ExpRec: Deep knowledge-awared question routing in software question answering community

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Software question answering community (SQAC) as an effective platform of knowledge sharing has achieved rapid development. In SQAC, one critical and challenging problem is question routing (or expert recommendation). To solve this problem, previous approaches focus on learning the relevance between the question and answerers. However, such approaches usually suffer from the data sparsity and noise issues which may reduce the accuracy of the question routing. Moreover, previous approaches also ignored the response quality and timeliness of the question routing. To tackle those issues, we study the question routing problem from two aspects: 1) the answerer’s relevance to the given question, and 2) the answerer’s capability. We first propose a deep knowledge-awared question routing framework (termed ExpRec) which leverages the attentive embedding propagates and their high-order connectivities to learn the answerer’s relevance to the given question. Then we explicitly model the answerer’s capability and incorporate it with the answerer’s relevance to the given question. Finally, to evaluate the performance of ExpRec, we conduct extensive experiments on two real-world datasets. The experimental results show that ExpRec outperforms other five state-of-the-art approaches significantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://stackoverflow.com/

  2. https://www.csdn.net/

  3. https://segmentfault.com/

  4. Stack Overflow has over 100 million registered users and exceeded 21 million questions in November 2021. Available at https://en.wikipedia.org/wiki/Stack_Overflow

  5. https://en.wikipedia.org/wiki/JSON_Web_Token

  6. https://www.tutorialspoint.com/wcf/wcf_security.htm

  7. https://dotnet.microsoft.com/apps/aspnet

  8. https://github.com/gamboviol/bpr

  9. https://archive.org/details/stackexchange

  10. SegmentFault is a famous software question answering community in China. Available at https://segmentfault.com/

  11. https://github.com/summanlp/textrank

  12. https://github.com/fxsjy/jieba/

  13. https://www.tensorflow.org/

  14. https://docs.w3cub.com/tensorflowpython/tf/contrib/layers/xavier_initializer

  15. https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/contrib/layers/python/layers/initializers.py

  16. https://en.wikipedia.org/wiki/P-value

  17. https://rdrr.io/cran/rcompanion/man/cliffDelta.html

  18. https://en.wikipedia.org/wiki/Effect_size

References

  1. Liu Z, Li K, Qu D (2017) Knowledge graph based question routing for community question answering. In: International conference on neural information processing, pp 721–730. https://doi.org/10.1007/978-3-319-70139-4_73

  2. Gao L, Gan Y, Yao Z, Zhang X (2021) A user-knowledge dynamic pattern matching process and optimization strategy based on the expert knowledge recommendation system. Appl Intell, 1–13. https://doi.org/10.1007/s10489-021-02289-3

  3. Sun J, Moosavi S, Ramnath R, Parthasarathy S (2018) Qdee: question difficulty and expertise estimation in community question answering sites. In: Twelfth international AAAI conference on web and social media

  4. Kundu D, Mandal DP (2019) Formulation of a hybrid expertise retrieval system in community question answering services. Appl Intell 49(2):463–477. https://doi.org/10.1007/s10489-018-1286-z

    Article  Google Scholar 

  5. Zhang X, Li S, Sha L, Wang H (2017) Attentive interactive neural networks for answer selection in community question answering. In: Thirty-first AAAI conference on artificial intelligence. https://doi.org/10.5555/3298023.3298080

  6. Fu C (2020) User correlation model for question recommendation in community question answering. Appl Intell 50(2):634–645. https://doi.org/10.1007/s10489-019-01544-y

    Article  Google Scholar 

  7. Van Dat N, Van Toan P, Thanh TM (2021) Solving distribution problems in content-based recommendation system with gaussian mixture model. Appl Intell, 1–13. https://doi.org/10.1007/s10489-021-02429-9

  8. Li Z, Jiang J-Y, Sun Y, Wang W (2019) Personalized question routing via heterogeneous network embedding. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 192–199. https://doi.org/10.1609/aaai.v33i01.3301192

  9. Lyu S, Ouyang W, Wang Y, Shen H, Cheng X (2019) What we vote for? answer selection from user expertise view in community question answering. In: The world wide web conference, pp 1198–1209. https://doi.org/10.1145/3308558.3313510

  10. 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 

  11. Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 974–983. https://doi.org/10.1145/3219819.3219890

  12. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2020.2978386

  13. Qian Y, Tang J, Wu K (2018) Weakly learning to match experts in online community. In: Proceedings of the 27th international joint conference on artificial intelligence (IJCAI), pp 3841–3847. https://doi.org/10.24963/ijcai.2018/534

  14. Yuan S, Zhang Y, Tang J, Hall W, Cabotà JB (2020) Expert finding in community question answering: a review. Artif Intell Rev 53(2):843–874. https://doi.org/10.1007/s10462-018-09680-6

    Article  Google Scholar 

  15. Yang X, Khabsa M, Wang M, Wang W, Awadallah AH, Kifer D, Giles CL (2019) Adversarial training for community question answer selection based on multi-scale matching. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 395–402. https://doi.org/10.1609/aaai.v33i01.3301395

  16. Deng Y, Lam W, Xie Y, Chen D, Li Y, Yang M, Shen Y (2020) Joint learning of answer selection and answer summary generation in community question answering. In: AAAI, pp 7651–7658. https://doi.org/10.1609/aaai.v34i05.6266

  17. Kundu D, Pal RK, Mandal DP (2021) Time-aware hybrid expertise retrieval system in community question answering services. Appl Intell, 1–18. https://doi.org/10.1007/s10489-020-02177-2

  18. Xie Y, Shen Y, Li Y, Yang M, Lei K (2020) Attentive user-engaged adversarial neural network for community question answering. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 9322–9329. https://doi.org/10.1609/aaai.v34i05.6472

  19. Zhang W, Deng Y, Lam W (2020) Answer ranking for product-related questions via multiple semantic relations modeling. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, pp 569–578. https://doi.org/10.1145/3397271.3401166

  20. Nie L, Li Y, Feng F, Song X, Wang M, Wang Y (2020) Large-scale question tagging via joint question-topic embedding learning. ACM Trans Inf Syst (TOIS) 38 (2):1–23. https://doi.org/10.1145/3380954

    Article  Google Scholar 

  21. Zhao J, Guan Z, Sun H (2019) Riker: Mining rich keyword representations for interpretable product question answering. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1389–1398. https://doi.org/10.1145/3292500.3330985

  22. Yang M, Chen L, Chen X, Wu Q, Zhou W, Shen Y (2019) Knowledge-enhanced hierarchical attention for community question answering with multi-task and adaptive learning. In: IJCAI, pp 5349–5355. https://doi.org/10.24963/ijcai.2019/743

  23. Huang C, Yao L, Wang X, Benatallah B, Zhang X (2020) Software expert discovery via knowledge domain embeddings in a collaborative network. Pattern Recogn Lett 130:46–3. https://doi.org/10.1016/j.patrec.2018.10.030

    Article  Google Scholar 

  24. Zhang X, Cheng W, Zong B, Chen Y, Xu J, Li D, Chen H (2020) Temporal context-aware representation learning for question routing. In: Proceedings of the 13th international conference on web search and data mining, pp 753–761. https://doi.org/10.1145/3336191.3371847

  25. Yang Y, Zhu Y, Li Y (2021) Personalized recommendation with knowledge graph via dual-autoencoder, Appl Intell, 1–12. https://doi.org/10.1007/s10489-021-02647-1

  26. Tang X, Wang T, Yang H, Song H (2019) Akupm: Attention-enhanced knowledge-aware user preference model for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1891–1899. https://doi.org/10.1145/3292500.3330705

  27. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Atetntion is all you need. In: Advances in neural information processing systems, pp 5998–6008. https://doi.org/10.5555/3295222.3295349

  28. Liang S, Shao J, Zhang D, Zhang J, Cui B (2021) Drgi: Deep relational graph infomax for knowledge graph completion. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3110898

  29. Wang X, He X, Cao Y, Liu M, Chua T-S (2019) Kgat: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 950–958. https://doi.org/10.1145/3292500.3330989

  30. Qiu J, Tang J, Ma H, Dong Y, Wang K, Tang J (2018) Deepinf: Social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2110–2119. https://doi.org/10.1145/3219819.3220077

  31. Jais IKM, Ismail AR, Nisa SQ (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2(1):41–46. https://doi.org/10.17977/um018v2i12019p41-46

    Article  Google Scholar 

  32. Zhang M, Li X, Yue S, Yang L (2020) An empirical study of textrank for keyword extraction. IEEE Access 8:178849–178858. https://doi.org/10.1109/ACCESS.2020.3027567

    Article  Google Scholar 

  33. Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2018) The impact of automated parameter optimization on defect prediction models. IEEE Trans Soft Eng 45(7):683–711. https://doi.org/10.1109/TSE.2018.2794977

    Article  Google Scholar 

  34. Arcuri A, Briand L (2011) A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: 2011 33Rd international conference on software engineering (ICSE), pp 1–10. https://doi.org/10.1145/1985793.1985795

  35. Xie X, Wang B, Yang X (2020) Softrec: Multi-relationship fused software developer recommendation. Appl Sci 10(12):4333. https://doi.org/10.3390/app10124333

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China under Grant 61502068, 32072787.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Linghai Road, Dalian, 116026, Liaoning, China

    Jiahui Liu & Ansheng Deng

  2. School of Computer Science and Engineering, Northeastern University, Chuangxin Road, Shenyang, 110169, Liaoning, China

    Xinqiang Xie

  3. College of Electrical and Information, Northeastern Agricultural University, Changjiang Road, Harbin, 150030, Heilongjiang, China

    Qiuju Xie

Authors
  1. Jiahui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ansheng Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinqiang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiuju Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiahui Liu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Deng, A., Xie, X. et al. ExpRec: Deep knowledge-awared question routing in software question answering community. Appl Intell 53, 5681–5696 (2023). https://doi.org/10.1007/s10489-022-03369-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03369-8

Keywords

Search

Navigation