一种基于桌面信息的个性化推荐方法

针对当前信息检索服务中存在的固有缺陷,提出了一种基于用户桌面信息抽取的个性化推荐方法.详细介绍了通过用户桌面资源信息抽取建立长期用户模型,以及通过工作场景信息抽取建立短期用户模型的算法.长期用户模型提供了完整全面的用户兴趣偏好信息,短期用户模型则为预测用户当前信息需求提供了依据.实验结果表明,基于用户桌面信息抽取的个性化推荐服务能较好地预测用户当前需求、具有良好的推荐效果.

An Approach to Personalize Recommendation Based on Desktop Information Extraction

In terms of the inherent detects in present information retrieval service,this paper proposed an approach to personalize recommendation based on desktop information extraction and introduced the algorithm to build the long-term user model based on desktop resources extraction,which provides information about the comprehensive interest preference of a user,and to establish the short-term user model based on working scenario information extraction,which serves as the basis to predict the users current information need.The experiment results showed that this approach could efficiently and effectively predict the users current information need and achieved pretty good recommendation results.