网络信息的高速增长导致了信息定位与获取的复杂化，通过分析目前网络的信息超载问题，对现有用户模型的表示法和更新技术进行比较，提出了一种以用户行为和用户操作的资料作为数据源，在用户兴趣漂移问题上采用兴趣衰减，即兴趣学习与常用的滑动窗口算法相结合的方法。最后以数字图书馆个性化搜索引擎中的用户建模系统的设计与实现为例，先后通过与普通向量空间表示模型的对比实验以及用户兴趣漂移时的跟踪学习，证明算法优化有利于更好地表达用户的兴趣，对提高个性化信息服务的质量有实用价值。%The rapid increasing of network information leads to the complexity of information positioning and acquisition. The existing expression method of user model and updating technology are compared by the analysis of information overloading problem of the current networks. A method of using user behavior and use operation data as the data source is proposed. The in⁃terest declining method is adopted for the user interest drift,which is integrated with interest learning and commen used sliding window algorithm. The design and implementation of user modeling system in personalized search engine of digital library is taken an example. The contrast experiments of the proposed model and the model expressed by common vector space,and tracing learning while user interest drift verifies that the optimized algorithm can better express the user interest,and has practical val⁃ue to improve the quality of high personalized information service.
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