A Model of a Trust-based Recommendation System on a Social Network

Abstract

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.

v3 revised as compared to v2 (figures updated, clarifications). Accepted for publication in J. Autonomous Agents and Multi-Agent Systems (2007)

References in corpus (6)

• Emergence of scaling in random networks
• The structure and function of complex networks
• Classes of behavior of small-world networks
• The scale-free topology of market investments
• Decision making dynamics in corporate boards
• Spread of decisions in the corporate board network

Cited by in corpus (20)

• Recommender Systems
• A blockchain based Secure and Trusted framework for Information Propagation on Online Social Networks
• A multi-theoretical kernel-based approach to social network-based recommendation
• Online Social Network Analysis: A Survey of Research Applications in Computer Science
• A trust-based recommendation method using network diffusion processes
• An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn
• Information Filtering on Coupled Social Networks
• Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature
• A Distributed Method for Trust-Aware Recommendation in Social Networks
• Personalizing Universal Recurrent Neural Network Language Model with User Characteristic Features by Social Network Crowdsouring
• GraphR: Accelerating Graph Processing Using ReRAM
• Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
• Group Recommendations: Axioms, Impossibilities, and Random Walks
• Recommendation systems in the scope of opinion formation: a model
• Perceiving the Social: A Multi-Agent System to Support Human Navigation in Foreign Communities
• Spectrum-enhanced Pairwise Learning to Rank
• Meta-Active Learning for Node Response Prediction in Graphs
• Building Bridges into the Unknown: Personalizing Connections to Little-known Countries
• Exploiting the `Web of Trust' to improve efficiency in collaborative networks
• A Heuristic Reputation Based System to Detect Spam activities in a Social Networking Platform, HRSSSNP