摘要
个性化推荐技术能够帮助用户快速方便地从大量的电子文献中获得感兴趣的文献,但传统的基于内容过滤的推荐算法不能反映用户对文献需求的兴趣变化,难以区分文献质量的高低。针对上述问题,提出了基于用户访问时间的数据权重和文献重要度,以便更好反应用户的兴趣以及文献质量的优劣,并将这两种度量引入到基于内容过滤的科技文献推荐算法中。实验和分析表明,改进后的算法比传统的内容过滤推荐算法在对文献推荐的准确度上有所提高。
Personalized recommendation techniques can help users to retrieval documents quickly and effectively from mass of electronic documents.However,existing content-based filtering algorithm don＇t reflect that the users information demands change over time,it is difficult to distinguish the literature quality difference.According to these questions,time-based data weight and literature importance are proposed,in order to response user＇s interest as well as literature quality fit and unfit quality better.On this basis,a content-based filtering of scientific literature recommendation algorithm is presented in this paper.Experimental evaluations and theoretical analysis demonstrate that the proposed algorithm can improve the accuracy of recommendations.
出处
《计算机技术与发展》
2011年第2期66-69,共4页
Computer Technology and Development
基金
贵州省2008年省级信息化专项基金项目(0830)
贵州省科技计划工业攻关基金项目(黔科合GY字[2008]3035)
关键词
科技文献
内容过滤
个性化推荐
时间权重
文献重要度
scientific literature
content-based filtering
personalized recommendation
time weight
literature importance
