Heterogeneity, quality, and reputation in an adaptive recommendation model

Abstract

Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [Medo et al., 2009] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a "good get richer" feature of the model and determine which attributes are necessary for a user to play a leading role in the network. We further investigate the filtering efficiency of the model as well as its robustness against malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can substantially improve the outcome.

References in corpus (3)

• Adaptive Coevolutionary Networks: A Review
• Role of Activity in Human Dynamics
• Adaptive model for recommendation of news

Cited by in corpus (9)

• Recommender Systems
• Small world yields the most effective information spreading
• Tag-Aware Recommender Systems: A State-of-the-art Survey
• Emergence of scale-free leadership structure in social recommender systems
• Enhancing topology adaptation in information-sharing social networks
• Adaptive social recommendation in a multiple category landscape
• Membership in social networks and the application in information filtering
• Impacts of suppressing guide on information spreading
• The role of taste affinity in agent-based models for social recommendation