本设计是在研究协同过滤算法的基础上设计实现一个网上书店系统。

The project is designing and implementing an on-line bookshop based on the collaborative filtering algorithms.

其中协同过滤被认为是至今为止在信息资源个性化推荐系统中最常用、最成功的一种个性化推荐技术。

All of them, collaborative filtering is regarded as most popular and successful personalized recommendatory algorithm in personalized recommendation system.

提出了一种基于粗集和模糊聚类相结合的协同过滤推荐算法，通过粗集理论自动填补空缺评分降低数据稀疏性；

This paper puts forward a collaborative filtering algorithm based on rough set and fuzzy clustering which automatically fills vacant ratings through rough set theory.

为了降低数据稀疏性的影响，提高推荐系统的推荐生成质量，提出了一种基于多层相似性用户聚类的协同过滤推荐算法。

To overcome the difficulty of data sparsity in recommendation systems, a collaborative filtering (CF) algorithm based on clustering basal users is presented.

实验结果证明了此方法不仅能很好的解决基于项目协同过滤算法带来的问题，而且还提高了推荐精度。

Experimental results showed that the integrated similarity algorithms efficiently deal with the problems associated with Item-based Collaborative Filtering algorithms as well as improving accuracy.

受电子商务研究领域中相关研究成果启发，我们尝试将协同过滤推荐技术引入学习资源的个性化推荐研究中。

Be inspired by the research achievement in e-commerce fields, we try to introduce the collaborative filtering technology into research of personalized recommendation of learning resources.

提出一种基于信任机制的协同过滤推荐算法，其中，直接信任度基于共同评价项目得出，推荐信任度通过对项目的预测得出。

This paper proposes a collaborative filtering recommendation algorithm based on trust mechanism. Direct trust is based on common rating data and indirect trust is based on the predict data.

那么，协同过滤和推荐会消失？

So, collaboratively filter and recommend or die?

协同过滤（CF ）系统毫无疑问是社会化网络的生命线。

The (CF) system is without a doubt the lifeblood of the social web.

这意味着，通过收集你是如何与该网站以及与其他用户交往的足够信息，协同过滤（CF ）系统可以为你推荐内容。

What this means is that by collecting enough information on how you interact with the site and with other users, the (CF) system can recommend content to you.