Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Recommender systems: a market-based design
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Market-based recommendation: Agents that compete for consumer attention
ACM Transactions on Internet Technology (TOIT)
A market-based approach to recommender systems
ACM Transactions on Information Systems (TOIS)
Decentralized voting with unconditional privacy
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An Improvement to Collaborative Filtering for Recommender Systems
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
A protocol for a distributed recommender system
Trusting Agents for Trusting Electronic Societies
A multi-agent recommender system for supporting device adaptivity in e-Commerce
Journal of Intelligent Information Systems
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Most of the existing recommender systems nowadays operate in a single organizational base, and very often they do not have sufficient resources to be used in order to generate quality recommendations. Therefore, it would be beneficial if recommender systems of different organizations can cooperate together sharing their resources and recommendations. In this paper, we propose a preliminary design of a distributed recommender system that consists of multiple recommender systems from different organizations. Moreover, a peer selection algorithm is also presented that allows a recommender system peer to select a set of other peers to cooperate with. The proposed selection mechanism not only ensures a high degree of user satisfaction to the generated recommendation, it also makes sure that every peer has been fairly treated and studied. The paper also further points out how the proposed distributed recommender system and the peer selection algorithm can provide a solution to the problem of resource lacking (e.g. cold start problem) and also enables recommender systems to provide recommendations with better novelty and quality to users.