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基于用户协作的新闻共享模型

A News Sharing Model Based on User Collaboration
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摘要 如何从互联网已有海量信息中为用户推荐符合其兴趣的新闻是一项极具挑战的工作。门户网站式的传统新闻传播方法没有考虑用户的个性化需求,因而无法给出合适的推荐。本文提出了一种基于用户协作的新闻共享模型,通过浏览器插件收集用户的浏览行为、分析用户对网页的隐性评价,并结合协同过滤算法实现相似用户间的新闻推荐。在原型系统中验证了本文所提方法的有效性,并分析了推荐算法的性能。 It is a real challenge to recommend news for users according to their interests from the existing mass information on the Internet.The traditional news propagation way,such as news on the portal sites,does not take the users＇ personal needs into consideration,which results in the low accuracy of the recommended news for users.In this article we propose a news sharing model based on the collaboration of users.More exactly,in this model,implicit ratings that the users give to the page are modeled from the users＇ behavior data,which are collected from the web browser plug-in,and then the news recommendations are shared among similar users based on these ratings through the algorithm of collaborative filtering.The method＇s availability is confirmed through a prototypic implementation of the model and the performance of the recommendation algorithm is also analyzed.
作者 冯大为 朱锐 王怀民
机构地区 国防科学技术大学计算机学院
出处 《计算机工程与科学》 CSCD 北大核心 2011年第5期165-170,共6页 Computer Engineering & Science
基金 国家973计划资助项目(2005CB321800 2005CB321801) 国家自然科学基金杰出青年基金(60625203)
关键词 浏览行为 用户兴趣 新闻推荐 协同过滤 browsing behavior user interests news recommendation collaborative filtering
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献11

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计算机工程与科学
2011年 第5期

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