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Role-Based Clustering for Collaborative Recommendations in Crowdsourcing System

  • Qiao Liao
  • Xiangmin Zhou
  • Daling WangEmail author
  • Shi Feng
  • Yifei Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11788)

Abstract

Crowdsourcing as a distributed problem-solving and business production model has attracted much attention in recent years. In crowdsourcing systems, task recommendation can help workers to select suitable tasks on crowdsourcing platforms as well as help requesters to receive good outputs. However, as one of the most successful recommendation approaches, current clustering-based models in crowdsourcing are challenged by multi-preference and cold-start problems. This paper proposes a role-based clustering model, which transforms a large-sparse worker-task rating matrix into a set of role-based clusters that are small, independent and rating intensive worker-task rating matrices, leading to better quality and performance in task recommendation. Specifically, we first cluster a worker-task rating matrix into a set of clusters in terms of the role identification and distribution operations. The clusters are further extended to include all their external worker (task) roles. Then, the task recommendation results with respect to a worker are generated by operating over the clusters involving the worker’s activities, which captures the worker’s preferences in multiple areas. Moreover, the model discovers the structure information from the clustering results and crowdsourcing datasets, by which tasks can be recommended to new workers interactively without their interest profiles. We evaluated our method over the benchmark dataset from NAACL 2010 workshop. The results show the high superiority of our proposed recommendation method over crowdsourcing platforms.

Keywords

Role-based clustering Task recommendation Cold-start Multi-preference Crowdsourcing system 

Notes

Acknowledgements

The work was supported by the National Key R&D Program of China under grant 2018YFB1004700, and National Natural Science Foundation of China (61772122, 61872074).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Qiao Liao
    • 1
    • 3
  • Xiangmin Zhou
    • 2
  • Daling Wang
    • 1
    Email author
  • Shi Feng
    • 1
  • Yifei Zhang
    • 1
  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.School of ScienceRMIT UniversityMelbourneAustralia
  3. 3.Tianjin Artificial Intelligence Innovation CenterTianjinChina

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