Aggregating crowd opinions using shapley value regression

Yuko Sakurai, Jun Kawahara, Satoshi Oyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Crowdsourcing is becoming increasingly popular in various tasks. Aggregating answers from workers in crowdsouring has been a widely used technique for providing many applications and services. To aggregate these answers, fair evaluation of workers is important to motivate them to give high quality answers. However, it is difficult to fairly evaluate workers if their answers show a high degree of correlation. In this paper, we propose to use the Shapley value regression as a means to address this problem. The regression technique is based on ideas developed from cooperative game theory to evaluate the relative importance of explanatory variables in reducing the error. We also exploit sparseness of worker collaboration graph to effectively calculate the Shapley value, since it requires an exponential computation time to calculate the Shapley value.

Original languageEnglish
Title of host publicationMulti-disciplinary Trends in Artificial Intelligence - 12th International Conference, MIWAI 2018, Proceedings
EditorsRainer Malaka, Manasawee Kaenampornpan, Duc Dung Nguyen, Nicolas Schwind
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030030131
Publication statusPublished - 2018
Externally publishedYes
Event12th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2018 - Hanoi, Viet Nam
Duration: Nov 18 2018Nov 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11248 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2018
Country/TerritoryViet Nam

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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