它测量了推荐系统产生推荐项目的占比。
It measures the percentage of items for which the recommender system can produce recommendations.
潘多拉(Pandora)——基于基因的推荐系统。
亚马逊的20- 30%销售额来自于它的推荐系统。
Amazon makes 20-30% of its sales from recommendations. Only 16% of people go to Amazon with explicit intent to buy something.
因此，推荐系统更好的任务是在某一刻充分利用店主的智慧。
So, the recommender is better tasked to take advantage of the wisdom of the place-owner "in the moment."
针对推荐质量和实时性要求，构建合理的推荐系统。
According to the problems of recommendation quality and real time requirement, the system must be built more reasonably.
推荐系统的应运而生，减轻了信息过量对人们的威胁。
Recommender systems which arising in this environment alleviate information overload facing individuals.
推荐系统；协同过滤；用户信任；恶意攻击；相似性。
Recommender System; Collaborative Filtering; User Trust; Malicious Attack; Similarity.
为了演示如何构建一个简单的推荐系统，我需要一些用户、项目和评分。
To demonstrate building a simple recommendation system, I need some users, items, and ratings.
让我们看看亚马逊推荐系统的各个模块，来了解他们到底是怎么工作的。
Lets take a look at various pieces of Amazon's recommendation system to get an insight on how they work. Here are the sections that are shown in the main area of my Amazon account when I login.
因此，一个推荐系统必须能够容纳积极参与其中的网站所有者和访客两方。
As such, a recommender must be able to accommodate the active participation of both the place-owner and visitor.
对基于聚类的协同过滤推荐系统的聚类算法进行了实现和评价。
Realize the clustering algorithm part of the recommendation system based on collaborative filtering and evaluate it.
或许推荐系统面临的最大问题，是需要大量的数据，以便能形成有效的推荐。
Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations.
但是，随着推荐系统的广泛应用，推荐算法的安全问题日益显现。
While, with the recommend systems widely used, the recommendation algorithm's security problem is increasingly appear.
不管将来怎样，亚马逊，潘多拉，美味书签都是拥有非凡推荐系统技术的典范。
Regardless of how things unfold in the future, Amazon, Pandora and del.icio.us are examples of extraordinary recommendation technologies.
可能会存在推荐系统潜在可以推荐的项目，但是算法不包括这些项目。
There might be items for which the system can potentially make a recommendation, but the algorithm never suggests those items.
当然，一个信息不足的推荐系统只是一个比较不理想的情况但可能仍然是有用的。
Of course, an uninformed recommender is just a degenerate case and may still be useful.
在活动介绍的内容中，提出了一些公司对于建造有效的推荐系统必须解决的几个问题。
In those presentations, there were some hints at the problems that these companies have to overcome to build an effective recommender system.
可以看看我更早的一篇博文：《攻击推荐系统》，讨论了来自同几个作者的一篇文章。
Please see also my earlier post, "Attacking recommender systems", that discusses another paper by some of the same authors.
有各种各样的情形可供访问者进行互动，但在大部分推荐系统中却几乎没有什么互动。
A broad array of modalities are available for visitor interaction, but few if any are available in most recommendation systems.
目录覆盖率测量的是推荐系统做出的推荐项目占全部可用项目个数的比值。
Catalog coverage measures the percentage of items for which the recommender system has ever made recommendations from the total available number of items.
推荐系统的评估可以考虑各种不同的方面，亦即，功能性的和非功能性的。
Recommender systems might be evaluated against various aspects of a recommender system, namely, functional and non-functional.
因而，能帮助用户更为快捷地找到所需信息的服务推荐系统得到了广泛关注。
Thus service recommendation systems which could help users more quickly find the information they need receive extensive attention.
推荐系统可以使用的几种主要的评测方法包括离线实验，用户调研和在线实验。
The evaluation in the recommender systems domain might be done utilizing several principal approaches, namely, off-line experiment, user studies and online experiments.
浏览过程对推荐系统来说是一个黄金机会，因为用户并不是专注于某一件东西——她需要建议。
It is the browsing that holds the golden opportunity for a recommendation system, because the user is not focused on finding a specific thing - she is open to Suggestions.
此方法的目的在于对推荐系统执行用户调研或者在线评估之前过滤掉性能较差的算法。
The goal of this approach is to filter out algorithms which have poor performance before the recommender system will be evaluated with a user study or online type evaluations.
推荐系统扮演着一个公司中销售员、代理商的角色，他们可以和顾客进行一对一地接触。
Recommenders play the role of the salesperson, the agent in the company who has one-on-one contact with each shopper.
所以，del. icio . us的方法极有可能被用于构造一个自组织分类和推荐系统。
So the del.icio.us approach holds intriguing possibilities of self-organizing classification and recommendation systems.
通过足够的用户量和更多打磨，社会化标签可以产生一个对书籍、红酒和音乐都很管用的推荐系统。
With enough users and more tweaking, social tagging can result in a system that works equally well for books, wine and music.
通过足够的用户量和更多打磨，社会化标签可以产生一个对书籍、红酒和音乐都很管用的推荐系统。
With enough users and more tweaking, social tagging can result in a system that works equally well for books, wine and music.
应用推荐