Adaptive model for recommendation of news Export

@article{Medo2009, 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.}, archivePrefix = {arXiv}, author = {Medo, Matus and Zhang, Yi-Cheng and Zhou, Tao}, citeulike-article-id = {5986148}, citeulike-linkout-0 = {http://arxiv.org/abs/0910.3490}, citeulike-linkout-1 = {http://arxiv.org/pdf/0910.3490}, day = {19}, eprint = {0910.3490}, keywords = {interestingness, liquidpub, news, recommender-systems, user-behavior}, month = {Oct}, posted-at = {2009-10-22 18:34:22}, priority = {2}, title = {Adaptive model for recommendation of news}, url = {http://arxiv.org/abs/0910.3490}, year = {2009} }

TY - JOUR ID - Medo2009 L3 - citeulike-article-id:5986148 N2 - 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. TI - Adaptive model for recommendation of news KW - interestingness KW - liquidpub KW - news KW - recommender-systems KW - user-behavior AU - Medo, Matus AU - Zhang, Yi-Cheng AU - Zhou, Tao PY - 2009/Oct/19/ UR - http://arxiv.org/abs/0910.3490 ER -