Mining collective intelligence in diverse groups-Reference-Cited by-同舟云学术

Mining collective intelligence in diverse groups

Published:2013 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 22nd international conference on World Wide Web - WWW '13
language:
Short-container-title:

Author:

Qi Guo-Jun¹,Aggarwal Charu C.²,Han Jiawei¹,Huang Thomas¹

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

2. IBM, Yorktown Heights, IL, USA

Publisher

ACM Press

Reference19 articles.

1. Y. Bachrach, T. Minka, J. Guiver, and T. Graepel. How to grade a test without knowing the answers - a bayesian graphical model for adaptive crowdsourcing and aptitude testing. In Proc. of International Conference on Machine Learning, 2012.

2. M. Bilgic, G. Namata, and L. Getoor. Combining collective classification and link prediction. In Workshop on Mining Graphs and Complex Structures (at ICDM), 2007.

3. A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Physical Review E, 70:066111, 2004.

4. X. L. Dong, L. Berti-Equille, and D. Srivastava. Integrating conflicting data: The role of source dependence. In Proc. of International Conference on Very Large Databases, August 2009.

5. A. Galland, S. Abiteboul, A. Marian, and P. Senellart. Corroborating information from disagreeing views. In Proc. of ACM International Conference on Web Search and Data Mining, February 2010.

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing;ACM Transactions on Embedded Computing Systems;2024-01-10

2. Turning the Cacophony of the Internet's Tower of Babel into a Coherent General Collective Intelligence;International Journal of Crowd Science;2023-06

3. Multi-Source Data Repairing: A Comprehensive Survey;Mathematics;2023-05-16

4. Label Enhanced Graph Attention Network for Truth Inference;Artificial Neural Networks and Machine Learning – ICANN 2023;2023

5. A Joint Maximum Likelihood Estimation Framework for Truth Discovery: A Unified Perspective;IEEE Transactions on Knowledge and Data Engineering;2022