Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Reasoning about knowledge
Communications of the ACM
PHOAKS: a system for sharing recommendations
Communications of the ACM
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Yenta: a multi-agent, referral-based matchmaking system
AGENTS '97 Proceedings of the first international conference on Autonomous agents
An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
JXTA: A Network Programming Environment
IEEE Internet Computing
Recommender systems: a market-based design
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Dynamic Pricing Mechanisms for P2P Referral Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Trustworthy knowledge diffusion model based on risk discovery on peer-to-peer networks
Expert Systems with Applications: An International Journal
A Fair Peer Selection Algorithm for an Ecommerce-Oriented Distributed Recommender System
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Risk discovery based on recommendation flow analysis on social networks
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
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We present a domain model and protocol for the exchange of recommendations by selfish agents without the aid of any centralized control. Our model captures a subset of the realities of recommendation exchanges in the Internet. We provide an algorithm that selfish agents can use for deciding whether to exchange recommendations and with whom. We analyze this algorithm and show that, under certain common circumstances, the agents' rational choice is to exchange recommendations. Finally, we have implemented our model and algorithm and tested the performance of various populations. Our results show that both the social welfare and the individual utility of the agents is increased by participating in the exchange of recommendations.