数据稀疏性是协同过滤系统面临的一个巨大挑战。
Data sparseness is a serious problem in collaborative filtering system.
一旦内容被推荐至首页，协同过滤系统的工作就算完成了。
Once the content is promoted to the front page, the system's job is done.
协同过滤系统的这两个方面会产生两个非常不同的重要效果。
The two aspects of the (CF) system result in two very different and important results.
用户相似度计算在协同过滤系统、用户推荐系统以及社交网络中有着非常重要的作用。
User similarity computing plays a very important role in collaborative filtering systems, user recommendation systems as well as social network services.
正如你从上述中所看到的，如果没有一个推荐引擎(如看到的Flickr)这当然也有可能是一个良好的协同过滤系统。
As you can see from above, it is certainly possible to have a good collaborative filtering system without a recommendation engine (as seen in Flickr).
协同过滤系统能让你摆脱垃圾邮件和一些无创造性的思想，但它是不是最好的，因为它依赖于平均水平，而不是直接依赖于每一个参与者。
The system works in that you get rid of spam and unoriginal thought, but it isn't the best because it relies on averages rather than direct preferences of each participant.
协同过滤系统的第二个效果是收集的信息是基于哪种内容、你喜欢还是不喜欢的评注，并根据您提交并参加投票的习惯，这些正是用户数据概况。
The second aspect of the (CF) system collects information on what kind of content and commentary you like and dislike, and based on your submission and voting habits, it does user-data-profiling.
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.
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.
极少有系统把元数据和内容一起用来做协同过滤。
Very few systems now are combining metadata or content with collaborative filtering.
一个很重要的事实，许多社会化网站并没有意识到这点，即协同过滤(CF)系统并不能根据您的喜好自动匹配内容，它有天然的缺陷。
The important thing, one that not many social sites realize, is that a (CF) system that doesn't automatically match content to your preferences, is inherently flawed.
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.
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.
其中，个性化推荐系统中的协同过滤推荐是迄今为止应用最广泛、最成功的推荐技术。
The collaborative filtering for the personalized recommendation is by far the most widely used and the most successful personalized recommender technology.
众多个性化推荐技术中协同过滤可谓一枝独秀，该算法引领了当今各大电子商务平台的推荐系统的发展趋势。
Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms.
协同过滤推荐算法是在电子商务推荐系统中最成功的技术之一。
Collaborative filtering recommendation algorithm is one of the most successful technologies in thee-commerce recommendation system.
电子商务系统规模的日益扩大，协同过滤推荐方法也面临诸多挑战：推荐质量、可扩展性、数据稀疏性、冷开始问题等等。
But, with expansion of E-commerce system's size, collaborative filtering approach suffer from many challenges, for instance, quality of recommendations, scalability, sparsity, cold-start problem.
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 is the most widely used and successful technology for personalized recommender systems. However it faces challenges of scalability and recommendation accuracy.
电子商务推荐系统中协同过滤已成为目前应用最广泛、最成功的推荐方法。
In E-commerce recommender system, collaborative filtering technology is the most popular and successful method at present.
为解决协同过滤推荐中“稀疏”和“冷开始”问题，提高推荐精度，提出了基于隐式评分的推荐系统。
Recommendation system based on implicit rating was proposed to improve the precision and solve the problems of "scarcity" and "cold-start".
最后利用实际网站数据对基于聚类的协同过滤推荐系统的聚类算法进行了实现，给出了系统试验结果，并对结果做出解释和评价。
Realize the system based clustering algorithm part of the recommendation on collaborative filtering and evaluate it, at last gives out the result of test with real data and try to explain it.
摘 要：电子商务推荐系统中协同过滤已成为目前应用最广泛、最成功的推荐方法。
Absrtact: In E- commerce recommender system, collaborative filtering technology is the most popular and successful method at present.
但是随着用户数量和系统规模的不断扩大，协同过滤推荐技术将面临严重的数据稀疏性、超高维、冷启动和实时推荐等方面的挑战。
However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items.
但是随着用户数量和系统规模的不断扩大，协同过滤推荐技术将面临严重的数据稀疏性、超高维、冷启动和实时推荐等方面的挑战。
However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items.
应用推荐