中文摘要：

随着互联网的发展，大量商品信息不断涌现，从而产生了信息过载问题。推荐系统作为解决此问题的有效手段，近年来得到快速发展。现存方法大多以用户行为和商品内容相似性为基础，利用用户购买记录和商品描述信息来产生推荐结果。事实上，用户的购买行为与时间也有着密切的联系。例如，最近购买的商品往往更能体现用户的当前兴趣。因此，在传统基于相似性推荐的基础上，本文提出一种基于时间特性的二部图推荐方法，通过调整初始资源权重分布体现用户兴趣随时间的变化趋势。实验证明，本文提出的方法在面向时间的Top—N命中率上有较大幅度提升。本文工作不仅对现有推荐算法的效果提高具有实际意义，对推荐系统在真实商业环境中的应用也有很大促进作用。

英文摘要：

With the rapid development of the World Wide Web,a large amount of trading intormation is available on the Internet. This abundance of information has created the need to help users find resources that match their individual goals and interests. This problem has been in the focus of recent research and an approach of recommendation system has been proposed. In the traditional system, users are provided with assistance in making selections according to the similarity of users＇ behavior and the products＇ in-formation. Actually ,users＇ interests vary gradually over time and their recent activities reveal their cur-rent interests. In this paper,a bipartite graph recommendation method is proposed based on time proper-ty,in which the time variation is taken into account and a new approach of initial resource adjustment is adopted to endow the recommended result with timeliness. Experiments show notable improvement on the Top-N hits metric. This method is not only of real value for improving the performance of recommen-dation system, but also has an active effect on the applications of recommendation system in E-Com-merce.