一种融合读者心情要素的新闻推送方法

互联网技术的飞速发展增强了用户与网络新闻间的交互性,使得网络新闻不仅包含传统的新闻内容和时间信息,还包含读者心情等交互信息。如何充分挖掘新闻特性,为用户提供便捷的浏览体验已逐渐成为新闻相关领域的研究热点。为方便用户通过输入查询词和心情浏览感兴趣的新闻,该文在考虑新闻的传统特性的同时,融合读者心情要素,提出一种全新的新闻推送方法。该方法重点研究依据读者心情的新闻排序算法,并考虑新闻内容与用户查询的主题相关性,以及新闻重要程度随时间衰减的特性,最终实现一种全新的新闻推送模式。基于所提方法,该文设计了一个融合读者心情要素的新闻推送系统,验证了该方法的有效性。

A Novel News Recommendation Method by Integrating Reader Mood

The rapid development of Internet technologies enhancesthe interrelationship between the users and the online news. Besides the traditional characteristics of content and time information in the news, the readers interactive information such as readers mood is also considered as a characteristics of the news. Recently, it has become a challenging task to sufficiently explore these characteristics to facilitate users browsing experience in news. In this study, we propose a novel news recommendation method which integrates the reader mood information as well as traditional news information such as content and time. The proposed method studies the news ranking algorithm according to the readers mood, the relevance between queries and news content as well as the importance decreasing along with time drifting. Additionally, we build a novel news recommendation system, which demonstrates the effectiveness of the proposed method.
Key wordsNews recommendation; news characteristics; reader mood; semi-supervised ranking algorithm; Negative correlation constraint