协作过滤是应用最为广泛的推荐技术，通常提供预测评分作为推荐。
Collaborative filtering, which is widely used recommendation algorithm, usually provides predicted ratings as recommendation.
提出一种新的协作过滤算法，采用概率形式，即预测用户喜欢商品的概率来推荐。
A new algorithm is proposed, which USES a probability value as the output showing the chance that a user might like an item.
另一个不太引人注意的网站个人化的手段是协作过滤软件，它驻留在网站中，跟踪用户动向。
A less obvious means of Web personalization is collaborative-filtering software that resides on a Web site and tracks users' movements.
重点介绍了一种叫做信息接种的协作过滤算法，它使邮件用户实现协作，提高垃圾邮件过滤器的准确率。
We gives a collaborative algorithms named message inoculation which make the mail users sharing information so as to improve the accuracy of a filter.
实验结果表明，改进的协作过滤算法优于基于用户的协作过滤算法；结合两种过滤技术后的系统具有更好的性能。
The results of experiment prove that improved filtering algorithm is better than user-based filtering algorithms and combined filtering approach has better system performance.
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。
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。
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.
应用推荐