A Recommendation of Crowdsourcing Workers Based on Multi-community Collaboration

  • Zhifang Liao
  • Xin Xu
  • Peng Lan
  • Jun LongEmail author
  • Yan Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)


Currently there are problems such as fuzzy workers’ characteristics and complex human relations existing on many crowdsourcing platforms, which lead to the difficulty in the recommendation of workers to complete tasks on crowdsourcing platforms. Aiming at worker recommendations in categorical tasks on crowdsourcing platforms, this paper proposes a recommendation considering workers’ multi-community characteristics. It takes factors such as worker’s reputation, preference and activity into consideration. Finally, based on the characteristics of community intersections, it recommends Top-N workers. The results show the recommendations generated by the algorithm proposed in this paper performs the best comprehensively.


Crowdsourcing Recommendation Community discovery 


  1. 1.
    Kucherbaev, P., Daniel, F., Tranquillini, S., Marchese, M.: Crowdsourcing processes: a survey of approaches and opportunities. IEEE Internet Comput. 20(2), 50–56 (2016)CrossRefGoogle Scholar
  2. 2.
    Howe, J.: The rise of crowdsourcing. Wired Mag. 14(6), 1–4 (2006)Google Scholar
  3. 3.
    Wang, Z., Sun, H., Fu, Y., Ye, L.: Recommending crowdsourced software developers in consideration of skill improvement. In: 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 717–722 (2017)Google Scholar
  4. 4.
    Zhang, Y., Qian, Y., Wang, Y.: A recommendation algorithm based on dynamic user preference and service quality. In: 2018 IEEE International Conference on Web Services (ICWS), pp. 91–98 (2018)Google Scholar
  5. 5.
    Qiuyan, Z., Yuan, Z., Chen, L.I., Yueyang, L.I.: Task recommendation method based on workers’ interest and competency for crowdsourcing. Syst. Eng. Theory Pract. 37, 3270–3280 (2017)Google Scholar
  6. 6.
    Liao, Z., Zeng, Z., Zhang, Y., Fan, X.: A data-driven game theoretic strategy for developers in software crowdsourcing: a case study. Appl. Sci. 9(4), 721 (2019)CrossRefGoogle Scholar
  7. 7.
    Hu, H., Zheng, Y., Bao, Z., Li, G., Feng, J., Cheng, R.: Crowdsourced POI labelling: location-aware result inference and task assignment (2016)Google Scholar
  8. 8.
    Mao, K., Yang, Y., Wang, Q., Jia, Y., Harman, M.: Developer recommendation for crowdsourced software development tasks. In: 2015 IEEE Symposium on Service-Oriented System Engineering, pp. 347–356 (2015)Google Scholar
  9. 9.
    Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018)CrossRefGoogle Scholar
  10. 10.
    Zhang, X., Su, J.: An approach to task recommendation in crowdsourcing based on 2-tuple fuzzy linguistic method. Kybernetes 47(8), 1623–1641 (2018)CrossRefGoogle Scholar
  11. 11.
    Ye, B., Wang, Y.: CrowdRec: trust-aware worker recommendation in crowdsourcing environments. In 2016 IEEE International Conference on Web Services (ICWS), pp. 1–8 (2016)Google Scholar
  12. 12.
    Kim, H.C., Ghahramani, Z.: Bayesian classifier combination. In Artificial Intelligence and Statistics, pp. 619–627 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhifang Liao
    • 1
  • Xin Xu
    • 1
  • Peng Lan
    • 1
  • Jun Long
    • 1
    Email author
  • Yan Zhang
    • 2
  1. 1.Department of Software Engineering, School of Computer Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Glasgow Caledonian UniversityGlasgowUK

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