Adaptive model for recommendation of news

被引：72

作者：

Medo, Matus ^{[1
]}

Zhang, Yi-Cheng ^{[1
]}

Zhou, Tao ^{[1
,2
]}

机构：

[1] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland

[2] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China

来源：

EPL | 2009年 / 88卷 / 03期

基金：

瑞士国家科学基金会; 中国国家自然科学基金;

关键词：

SYSTEMS;

D O I：

10.1209/0295-5075/88/38005

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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. Copyright (C) EPLA, 2009

引用

页数：6

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