协同推荐技术是实现个性化推荐系统的一种有效方法。
So this paper presents the recommendation system into the E-learning platform, in order to accomplish the purpose of personalized service.
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.
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).
不管用什么方法，协同过滤或基于item相似的推荐都是不会被原谅的商业工具，假阳性般的错误会很快地让用户流失。
Regardless of the method, collaborative filtering or inherent properties of things - recommendations are an unforgiving business, where false positives quickly turn users off.
那么，协同过滤和推荐会消失？
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.
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.
用户评分矩阵稀疏问题影响协同过滤的推荐性能。
The sparse user-item matrix often hurts the performance of recommendation system.
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.
为了有效地解决协同过滤算法中新项目难以推荐的问题，文中提出了一种对项目矩阵进行划分的方法。
To efficiently resolve the problem that the new item is difficult to recommend in collaborative filtering algorithm. In this paper we propose a new method based item matrix partition.
提出一种基于项目特征模型的协同过滤推荐算法。
A collaborative filtering recommendation algorithm based on the item features model is proposed in this paper.
解决推荐问题有三个通常的途径：传统的协同过滤，聚类模型，以及基于搜索的方法。
There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods.
该模型的建立对于缓解协同过滤技术中存在的稀疏性问题、推荐的实时性问题有很大的帮助。
This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time.
利用协同过滤来产生推荐，很耗计算。
Using collaborative filtering to generate recommendations is computationally expensive.
挖掘结果表明，在数据极端稀疏的情况下，基于项目的协同过滤推荐方法明显的提高了推荐质量。
The result of mining shows that, in the case of the data extremely sparseness, project-based collaborative filtering recommendation method is effective to improve the recommended quality.
我们的算法，也就是商品到商品的协同过滤，符合海量的数据集和产品量，并能实时得到高品质的推荐。
Our algorithm, item-to-item collaborative filtering, scales to massive data sets and produces high-quality recommendations in real time.
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.
与在目标中找寻过去的相似点的基于内容的过滤不同，协同过滤通过找寻具有相同品位的访客开发出推荐的项目。
Instead of finding objects similar to those a visitor liked in the past, as in content-based filtering, collaborative filtering develops recommendations by finding visitors with similar tastes.
本研究推荐的用于人工地下水回灌的城市污水深度处理工艺是DGB吸附与聚合氯化铝混凝沉淀协同处理。
The recommended advanced treatment technology before artificial groundwater recharge in this study was DGB adsorption combined with coagulation by PAC .
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.
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.
实验结果表明，IAPCF算法比传统的基于项目的协同过滤算法具有更好的推荐精度。
The experiment results suggested that IAPCF could provide better recommendation results than the traditional item-based collaborative filtering algorithms.
协同过滤技术可以通过分析客户群共同的消费品味来形成推荐。
Collaborative Filtering (CF) is used for forming recommendation by analyzing the common "taste" Shared by a group of customers.
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.
实验结果表明，该算法比基于项目的协同过滤推荐算法在精确度上有所提高。
Furthermore, the results show that the accuracy of algorithm proposed here has somewhat increased compared with that of the collaborative filtering recommendation algorithm based on item.
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.
实验结果表明：基于项目矩阵划分的协同过滤算法有效地解决新项目推荐困难的问题，显示出了比传统推荐算法更好的推荐质量和扩展性。
Compared traditional collaborative filtering method, the experimental results show that our approach can find a solution to the problem of new item recommendation effectively.
实验结果表明：基于稀疏矩阵划分的个性化推荐算法在算法性能上优于传统协同过滤算法。
Moreover, compared traditional collaborative filtering method, the experimental results show the effectiveness and efficiency of our approach.
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.
协同过滤技术分为基于内存和基于模型两种，前者的推荐准确度更高，但可扩展性比后者低。
Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability.
应用推荐