Metadata Creation - tagging, curation and collaborative filtering.
元数据生成—标签，管理以及群体过滤。
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。
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。
It's a technique called collaborative filtering, and it works on the principle that the behavior of a lot of people can be used to make educated guesses about the behavior of a single individual.
这就是所谓的“协同筛选”技术，该项技术所依据的原则是：可以依据大多数人的行为对个体行为作出有根据的推测。
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。
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。
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。
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。
These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering.
这些不相关的项目相当于噪音数据，往往会干扰协同过滤的效果。
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。
Absrtact: In E- commerce recommender system, collaborative filtering technology is the most popular and successful method at present.
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。
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。
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。
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。
User similarity computing plays a very important role in collaborative filtering systems, user recommendation systems as well as social network services.
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。
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。
The experiment results suggested that IAPCF could provide better recommendation results than the traditional item-based collaborative filtering algorithms.
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。
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。
This paper puts forward a collaborative filtering algorithm based on rough set and fuzzy clustering which automatically fills vacant ratings through rough set theory.
提出了一种基于粗集和模糊聚类相结合的协同过滤推荐算法，通过粗集理论自动填补空缺评分降低数据稀疏性；
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。
Amazon (AMZN) calls this homegrown math "item-to-item collaborative filtering," and it's used this algorithm to heavily customize the browsing experience for returning customers.
亚马逊把这套自主研发的算法称为“从项目到项目的协同过滤算法”。依靠这套算法，亚马逊向回头客们提供了深度定制的浏览体验。
In order to evaluate our new collaborative filtering algorithm and combined approach, we have developed a Prototype System for Chinese computer science literature automatic filtering.
为了对我们提出的改进的协作过滤算法和结合过滤方法进行评价，我们研制了一个中文计算机科技文献自动过滤原型系统。
And point out the problems existed in the traditional collaborative filtering methods: ratings matrix sparsity, cold start, vulnerability, single interest model and scalability issues.
指出了传统协同过滤方法存在着评分矩阵稀疏、冷启动、易受攻击性、兴趣模型单一和可扩展性等问题。
This model may effectively improve the traditional collaborative filtering method used to compute the similarity between users, and enhances the accuracy of user similarity measurement.
该模型可有效地改进传统协同过滤模型相似性度量方法，提高了用户相似性度量准确性。
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