一种面向个性化服务的无需反例集的用户建模方法

随着WWW信息的快速增长 ,查找用户感兴趣的信息变得越来越耗时耗力。个性化服务能为不同的用户提供有针对性的服务 ,日益受到研究者的重视。用户建模是实现个性化服务的关键技术。传统的需要正、反例集作为训练例集的用户建模方法容易干扰用户的正常浏览 ,或者由于推断失误而引入噪声。基于遗传算法和k近邻方法提出了一种无需反例集的用户建模方法 ,该方法被应用于个性化信息过滤中。实验结果表明 ,基于无需反例集的用户建模方法的信息过滤算法可以达到 73 91%的过滤率和 94 4 4 %的过滤精度。无需反例集的用户建模方法是一种可行、高效的用户建模方法

A User Modeling Method without Negative Examples for Personalized Services

With the exponential growth of World Wide Web, it becomes more and more time and energy consuming for users to find what they're interested in It leads to a clear demand for personalized services, which can provide different users with different services User modeling is the key technology in implementing personalized services Conventional user modeling methods with both positive and negative examples will either interfere users' normal browsing or bring in noises A user modeling method without negative examples is presented A hybrid of genetic algorithms and kNN classifier are utilized to search the words describing users' interests The method is applied in personalized information filtering The experiments show that the filtering ratio and precision can be 73 91% and 94 44% respectively, which demonstrates that our user modeling method is feasible and efficient