The paper introduces e - commerce recommender systems and the typical technologies that are implemented in them , collaborative filtering technology and its two directions of existing algorithms 论文全面介绍了电子商务推荐系统及其典型的实现技术、协同过滤及已有协同过滤算法的两个方向。
In order to evaluate our new collaborative filtering algorithm and combined approach , we have developed a prototype system for chinese computer science literature automatic filtering 为了对我们提出的改进的协作过滤算法和结合过滤方法进行评价,我们研制了一个中文计算机科技文献自动过滤原型系统。
Through comparing and analyzing between item - based collaborative filtering algorithm and user - based collaborative filtering algorithm , we proposed an improved method of item - based collaborative filtering algorithm 通过对基于项和基于用户的协同过滤算法的比较与分析,提出了一个基于项的协同过滤改进算法。
Compared with traditional collaborative filtering techniques , recommendation systems based on web mining are convenient for users because user need n ' t to provide user - rating data explicitly 相对于传统协同过滤推荐技术而言,基于web挖掘的推荐系统框架不需要用户提供主摘少观的评价信息,因此用户使用起来比较方便。
5 . the thesis presents a new collaborative filtering algorithm , which can classify the users in electronic commerce , can perform personalization web pages recommendation and can provide individual service 通过协作聚类可以对电子商务的用户进行分类,针对不同类型的用户进行不同的页面推荐,实现了电子商务的个性化服务。
Collaborative filtering (CF) is a technique used by some recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.