Adaptive model for recommendation of news

TL;DR

This paper introduces an adaptive news recommendation model that combines user rating similarities with epidemic-like news spreading on evolving networks, outperforming traditional popularity-based methods in simulations.

Contribution

It presents a novel adaptive recommendation model integrating social spreading dynamics with user preferences, enhancing recommendation robustness and effectiveness.

Findings

Outperforms popularity-based recommendations in simulations.

Shows robustness against bias and malicious behavior.

Provides a general social mechanism for recommender systems.

Abstract

Most news recommender systems try to identify users' interests and news' attributes and use them to obtain recommendations. Here we propose an adaptive model which combines similarities in users' rating patterns with epidemic-like spreading of news on an evolving network. We study the model by computer agent-based simulations, measure its performance and discuss its robustness against bias and malicious behavior. Subject to the approval fraction of news recommended, the proposed model outperforms the widely adopted recommendation of news according to their absolute or relative popularity. This model provides a general social mechanism for recommender systems and may find its applications also in other types of recommendation.