A vertical news recommendation system: CCNS—An example from Chinese campus news reading system

Shan Jiang, Wenxing Hong
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引用次数: 9

Abstract

News recommendation systems are widely used to address the information overloading problem. Many Web-based news reading services, like Google News and Yahoo! News, have become increasingly prevalent as they help users find interesting articles from news providers that match the users' preference. However, few research efforts have been reported on campus news recommendation. Different from news articles, news from vertical systems is often short with limited topic scope, targeting at specific audience. To address the aforementioned characteristics, in the paper, we develop a hybrid recommendation system for campus news by integrating different recommendation algorithms using linear combination. Offline and online experiments are conducted to evaluate the system effectiveness.
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垂直新闻推荐系统:ccns——以中国校园新闻阅读系统为例
新闻推荐系统被广泛用于解决信息过载问题。许多基于网络的新闻阅读服务,如Google news和Yahoo!新闻,已经变得越来越普遍,因为它们帮助用户从符合用户偏好的新闻提供者那里找到有趣的文章。然而,关于校园新闻推荐的研究鲜有报道。与新闻文章不同,垂直系统的新闻通常篇幅较短,主题范围有限,针对特定受众。针对上述特点,本文采用线性组合的方法对不同的推荐算法进行集成,开发了一种校园新闻混合推荐系统。进行了离线和在线实验,以评估系统的有效性。
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