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传统的协作过滤方法存在的主要问题是需要人为地提供评价,论文设计的协作过滤方法对其进行了改进,根据用户模式自动获取用户评价,构建评价矩阵。将设计的协作过滤方法应用到个性化信息推荐,提出一种基于协作过滤的Web智能信息推荐方法(WIIRM)。WIIRM考虑用户访问页面的时间特性,不需要用户注册,在推荐时考虑页面的新颖性,同时实现离线处理与在线推荐的结合。实验结果表明,WIIRM是有效的。
The main problem existing in the traditional collaborative filtering method is that it needs artificially to provide the evaluation. The collaborative filtering method of the thesis design improves it, automatically obtains the user’s evaluation according to the user’s mode, and constructs the evaluation matrix. The collaborative filtering method is applied to personalized information recommendation, and a Web Intelligence Information Recommendation Method (WIIRM) based on collaborative filtering is proposed. WIIRM takes into account the time characteristics of the user’s visit to the page, does not require user registration, considers the novelty of the page when recommending, and achieves a combination of offline processing and online recommendation. The experimental results show that WIIRM is effective.