Heterogeneity, quality, and reputation in an adaptive recommendation model
Physics and Society
2015-03-17 v1 Social and Information Networks
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.
Cite
@article{arxiv.1012.1099,
title = {Heterogeneity, quality, and reputation in an adaptive recommendation model},
author = {Giulio Cimini and Matus Medo and Tao Zhou and Dong Wei and Yi-Cheng Zhang},
journal= {arXiv preprint arXiv:1012.1099},
year = {2015}
}