First , two kinds of text filtering approach are described , then collaborative filtering technologies are deeply studied 本文首先对文本过滤的两种形式进行了描述,然后对协作过滤技术进行了较深入的探讨。
It gives emphasis to analyzing the problems which collaborative filtering is facing when it is applied in recommender systems and existing improved methods 着重分析了协同过滤在推荐系统中应用时所面临的问题,以及现有的解决方法。
But , when the system scale ( such as the number of customers or the types of products ) is very large , collaborative filtering faces great challenges 但是,当系统规模(用户数量、产品种类)很大时,推荐系统中的协同过滤技术面临着严峻的挑战。
We discuss how to get some important parameters " values of collaborative filtering algorithm through experiment . the experiment shows that this scheme is feasible 对于协同过滤算法,通过实验讨论了它的一些关键参数的选择依据,最后通过实验验证算法的可行性。
Aiming at some problems of collaborative filtering technologies , we have explored item - based collaborative filtering algorithm , which solves effectively sparsity and scalability problems 针对协作过滤方法的某些缺点,提出了一种改进的过滤算法-基于信息项的协作过滤算法。
Collaborative filtering (CF) is a technique used by some recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.