Adaptive model for recommendation of newsMatúš Medo1, Yi-Cheng Zhang1 and Tao Zhou1, 2
1 Physics Department, University of Fribourg - CH-1700 Fribourg, Switzerland
2 Department of Modern Physics, University of Science and Technology of China - Hefei 230026, PRC
received 3 July 2009; accepted in final form 15 October 2009; published November 2009
published online 12 November 2009
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.
89.65.-s - Social and economic systems.
89.75.Hc - Networks and genealogical trees.
89.20.Ff - Computer science and technology.
© EPLA 2009