Heterogeneity, quality, and reputation in an adaptive recommendation model

TL;DR

This paper analyzes an adaptive news recommender model based on epidemic spreading, exploring how user reputation and heterogeneity influence its effectiveness, robustness, and the emergence of influential users.

Contribution

It introduces the role of user reputation in the model, demonstrating improvements in filtering efficiency and robustness against malicious behaviors.

Findings

Reputation enhances recommendation quality

Heterogeneity affects user influence dynamics

Model is robust against spam and malicious attacks

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.