Metadata Creation - tagging, curation and collaborative filtering.
元数据生成—标签，管理以及群体过滤。
Data sparseness is a serious problem in collaborative filtering system.
数据稀疏性是协同过滤系统面临的一个巨大挑战。
Data sparsity problem is a potential challenge of collaborative filtering.
数据稀缺性问题是协同过滤技术面临的主要挑战。
This paper puts forward an model based on classify in collaborative filtering.
在协同过滤中，提出基于分类的协同过滤算法。
Very few systems now are combining metadata or content with collaborative filtering.
极少有系统把元数据和内容一起用来做协同过滤。
All this means that there's a ceiling to how accurate collaborative filtering can get.
所有这一切意味着“协同筛选”永远不会做到尽善尽美。
Recommender System; Collaborative Filtering; User Trust; Malicious Attack; Similarity.
推荐系统；协同过滤；用户信任；恶意攻击；相似性。
Using collaborative filtering to generate recommendations is computationally expensive.
利用协同过滤来产生推荐，很耗计算。
Unlike other algorithms, item-to-item collaborative filtering is able to meet this challenge.
与其他算法不同，商品到商品的协同过滤能满足这样的挑战。
Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering.
在此，我们就这些方法与我们的算法——我们称之为商品到商品的协同过滤——进行对比。
The other is collaborative filtering technology which can accomplish individual services for the websites.
另一类使用合作过滤技术实现网站个性化服务。
A collaborative filtering recommendation algorithm based on the item features model is proposed in this paper.
提出一种基于项目特征模型的协同过滤推荐算法。
Collaborative filtering works only as well as the data it has available, and humans produce noisy, low-quality data.
“协同筛选”的工作仅仅针对所有可用的数据，而人类却是制造那些杂乱无章、低质量数据的始作俑者。
Realize the clustering algorithm part of the recommendation system based on collaborative filtering and evaluate it.
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。
Collaborative filtering technology reveals a number of bottlenecks to be addressed in the face of current challenges.
协同过滤技术在面对当前的挑战时暴露出许多有待解决的瓶颈问题。
In E-commerce recommender system, collaborative filtering technology is the most popular and successful method at present.
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。
Collaborative filtering, which is widely used recommendation algorithm, usually provides predicted ratings as recommendation.
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。
This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time.
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。
Our algorithm, item-to-item collaborative filtering, scales to massive data sets and produces high-quality recommendations in real time.
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。
Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
针对协同过滤中的数据稀疏问题，提出了一种基于粗集的协同过滤算法。
This method can improve the traditional collaborative filtering methods, and can solve the single-interest model problem of traditional methods.
这种方法是对传统的协同过滤方法的改进，可以解决传统方法兴趣模型单一问题。
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).
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。
There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods.
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。
The collaborative filtering for the personalized recommendation is by far the most widely used and the most successful personalized recommender technology.
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。
Traditional collaborative filtering does little or no offline computation, and its online computation scales with the number of customers and catalog items.
传统的协同过滤只做很少或不做离线计算，其在线计算量取决于顾客和登记在册商品的数量。
To address these problems, a collaborative filtering recommendation algorithm combining probabilistic relational models and user grade (PRM-UG-CF) is presented.
针对传统协同过滤推荐算法的稀疏性、扩展性问题，提出了结合似然关系模型和用户等级的协同过滤推荐算法。
Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms.
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。
Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog.
与传统协同过滤不同，我们算法的在线计算规模，与顾客数量和产品目录中的商品数量无关。
Regardless of the method, collaborative filtering or inherent properties of things - recommendations are an unforgiving business, where false positives quickly turn users off.
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。
For example many social bookmarking and social news sites use community sentiment and collaborative filtering to help to highlight what is most interesting, useful or important.
比如许多社会化书签和社会化新闻网站都采用社区意见和协作性过滤来帮助强调突出最有趣、最有用和最重要的内容。
应用推荐