Are Recommender Systems Good for Libraries?
推荐系统适用于图书馆吗？
Recommender systems which arising in this environment alleviate information overload facing individuals.
推荐系统的应运而生，减轻了信息过量对人们的威胁。
Recommender systems may be used to analyze the preference of customer, recommend product to targeted customer.
电子商务网站可以使用推荐系统分析客户的消费偏好，向每个客户具有针对性地推荐商品。
Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations.
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。
The system can adapt the changes of user interests quickly, and is more exact than the existing recommender systems.
最后给出实验表明该系统能够准确表达用户兴趣，特别时在用户兴趣发生变化时比以往系统具有更高的准确性。
Please see also my earlier post, "Attacking recommender systems", that discusses another paper by some of the same authors.
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。
Some recommender systems require the user to manually enter a personal profile of interests, preferences, or expertise.
一些推荐系统需要用户手动输入一个包括个人爱好、兴趣或专长的个人信息文件。
Recommender systems might be evaluated against various aspects of a recommender system, namely, functional and non-functional.
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。
Personalized Recommender Systems emerge under the background of this, which becomes the research focus in the domestic and overseas.
个性化推荐系统在这样的背景下应运而生。
This gives secondary effects like very fast graph algos, recommender systems and OLAP-style analytics that are currently not possible with normal RDBMS setups.
此外，Neo4j还提供了非常快的图形算法、推荐系统和OLAP风格的分析，而这一切在目前的RDBMS系统中都是无法实现的。
While recommender systems are often designed to provide anonymous recommendations, referral Web is based on providing referrals via chains of named Individuals.
推荐系统更多是用来提供匿名推荐的，提名网通过知名的个人所构建的联系来产生提名。
The evaluation in the recommender systems domain might be done utilizing several principal approaches, namely, off-line experiment, user studies and online experiments.
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。
Collaborative filtering is the most widely used and successful technology for personalized recommender systems. However it faces challenges of scalability and recommendation accuracy.
协同过滤是个性化推荐系统中应用最广泛和最成功的推荐技术，但是它也面临着推荐准确度和可扩展性两大挑战。
Collaborative filtering recommendation algorithm can make choices based on the opinions of other people. It is the most successful technology for building recommender systems to date.
协同过滤是目前最成功的一种推荐算法，它能够基于其他用户的观点帮助人们作出选择。
Collaborative filtering recommendation algorithm can make choices based on the opinions of other people. It is the most successful technology for building recommender systems to date.
协同过滤是目前最成功的一种推荐算法，它能够基于其他用户的观点帮助人们作出选择。
应用推荐