Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (12): 106-113.doi: 10.11896/jsjkx.200300107

Special Issue: Software Engineering & Requirements Engineering for Complex Systems

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Software Crowdsourcing Task Recommendation Algorithm Based on Learning to Rank

YU Dun-hui1,2, CHENG Tao1, YUAN Xu1   

  1. 1 College of Computer and Information Engineering Hubei University Wuhan 430062,China
    2 Education Informationalization Engineering and Technology Center Wuhan 430062,China
  • Received:2020-03-18 Revised:2020-07-25 Online:2020-12-15 Published:2020-12-17
  • About author:YU Dun-hui,born in 1974Ph.Dprofessoris a member of China Computer Federation.His main research interests include service computing and big data.
    CHENG Tao,born in 1995M.S.candidate.His main research interests include big data and so on.
  • Supported by:
    Technology Innovation Special Program of Hubei Province(2018ACA13) and National Natural Science Foundation of China(61572371,61832014).

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Abstract: In order to realize software crowdsourcing task recommendation more effectivelyimprove the quality of software developmentrecommend suitable tasks for workersreduce the risk of workers' interests being damagedand achieve a win-win result for workers and crowdsourcing platformsa software crowdsourcing task recommendation method based on learning to rank is designed.Firstthe hidden features between workers and tasks are extracted based on the improved latent factor model.Thenthe model of learning to rank is improved by combining implicit informationand the extracted hidden features are ranked and trained to obtain the optimal ranking model.The ranking model sorts the test set tasks to get a task recommendation list to perform crowdsourcing task recommendation for workersand uses relevant evaluation indicators to verify the recommendation results.Experiments show that the proposed method can effectively improve the software crowdsourcing task recommendation accuracy.The NDCGMAPand Recall values of the recommended evaluation indicators reach 0.7220.3260.169respectively.Compared with the user-based collaborative filtering algorithmthe recommendation accuracy is improved by 18.6%.Compared with rank learning algorithm based on RankNet onlythe accuracy is improved by 10.2%which can effectively guide software crowdsourcing task recommendation.

Key words: Implicit feedback, Latent factor model, Learning to rank, Software crowdsourcing, Task recommendation

CLC Number: 

  • TP311.52
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