In our research , firstly , we analyze the influence factors in customer buying processing . we use collaborative filtering and agent technology to anticipate the social influence and psychology tendency 本文中我们首先分析了顾客购买过程的决策影响因素,特别是社会因素和心理因素,并将其与协作过滤和智能主体分别进行结合。
This advisory function resembles amazon ' s well - known book recommendation feature , which takes advantage of search and purchasing patterns of communities of users ? a process sometimes called collaborative filtering 这种谘询特性是利用使用者社群的查询与购买模式达成,和亚马逊知名的书籍推荐功能一样,有时也被称为协同筛选。
Experimental results show that this method is of low memory requirements and lower mean absolute error ( mae ) value , and provides better recommendation quality compared with traditional collaborative filtering algorithms 实验表明,这种方法的优点是低内存需求,具有较小的平均绝对偏差值,并且显示出了比传统推荐算法更好的推荐质量。
The aim to research product recommendation system is to improve the quality of product recommend , so that motivate and meet the need of customers ( in this essay , it is aim to the customers on - line ) . at present , the recommendation system mainly use the technology of collaborative filtering ( cf ) 对于商品推荐系统研究的目的就是要提高商品推荐的质量(成功率) ,以激发和满足顾客需要(本文论述的对象主要是针对网上购物的顾客) 。
Second , a new text filtering approach that combines content - based filtering with collaborative filtering is proposed , which makes full use of the advantages of content - based filtering and collaborative filtering and solves effectively early rater problem and improves system performance 本文还提出了一种结合内容过滤和协作过滤的文本过滤方法,该方法充分利用两种过滤技术的优点,有效地解决了早期级别等问题,使过滤系统的性能得到了提高。
Collaborative filtering (CF) is a technique used by some recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.