2006/11/30 by Frank E. Walter, Stefano Battiston, Frank Schweitzer
Computer Science · Physics and Astronomy · Psychology · #Affect (linguistics) #Artificial intelligence #Collaborative filtering #Complex Network Analysis Techniques #Computer science #Distributed computing #Filter (signal processing) #Knowledge management #Machine learning #Microeconomics #Network structure #Opinion Dynamics and Social Influence #Peer-to-Peer Network Technologies #Preference #Property (philosophy) #Psychology #Recommender system #Social media #Social network (sociolinguistics) #Social system #State (computer science) #World Wide Web #cs.IR #nlin.AO #physics.soc-ph
paper · pdf · doi:10.1007/s10458-007-9021-x
published as Journal of Autonomous Agents and Multi-Agent Systems, vol. 16, no. 1 (2008), pp. 57-74 · v3 revised as compared to v2 (figures updated, clarifications). Accepted for publication in J. Autonomous Agents and Multi-Agent Systems (2007)
arxiv created 2007/09/18 · openalex publication_date 2007/10/17 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agents.