Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
A trust-enhanced recommender system application: Moleskiing
Proceedings of the 2005 ACM symposium on Applied computing
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Input-agreement: a new mechanism for collecting data using human computation games
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
| Hi-index | 0.00 |
With the emergence of Web 2.0 applications, where information is not only shared across the internet, but also syndicated, evaluated, selected, recombined, edited, etc., quality emergence by collaborative effort from many users becomes crucial. However, users may have low expertise, subjective views, or competitive goals. Therefore, we need to identify cooperative users with strong expertise and high objectivity. As a first step towards this aim, we propose criteria for user type classification based on prior work in psychology and derived from observations in Web 2.0. We devise a statistical model for many different user types, and detection methods for those user types. Finally, we evaluate and demonstrate both model and detection methods by means of an experimental setup.