Communications of the ACM
Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Ranking systems: the PageRank axioms
Proceedings of the 6th ACM conference on Electronic commerce
Eliciting single-peaked preferences using comparison queries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dynamically learning sources of trust information: experience vs. reputation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
On the complexity of schedule control problems for knockout tournaments
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Preference functions that score rankings and maximum likelihood estimation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Two of a Kind or the Ratings Game? Adaptive Pairwise Preferences and Latent Factor Models
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Fair Seeding in Knockout Tournaments
ACM Transactions on Intelligent Systems and Technology (TIST)
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Complex social networks are typically used in order to represent and structure social relationships that do not follow a predictable pattern of behaviour. Due to their openness and dynamics, participants in these networks have to constantly deal with uncertainty prior to commencing in any type of interaction. Reputation appears as a key concept helping users to mitigate such uncertainty. However, most of the reputation mechanisms proposed in the literature suffer from problems such as the subjectivity in the opinions and their inaccurate aggregation. With these problems in mind, this paper presents a decentralized reputation mechanism based on the concepts of pairwise elicitation processes and knock-out tournaments. The main objective of this mechanism is to build reputation rankings from qualitative opinions, so getting rid of the subjectivity problems associated with the aggregation of quantitative opinions. The proposed approach is evaluated in a real environment by using a dataset extracted from MovieLens.