利用协同过滤来产生推荐，很耗计算。
Using collaborative filtering to generate recommendations is computationally expensive.
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.
在协同过滤中，提出基于分类的协同过滤算法。
This paper puts forward an model based on classify in collaborative filtering.
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.
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.
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.
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。
Unlike other algorithms, item-to-item collaborative filtering is able to meet this challenge.
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.
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.
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。
Collaborative filtering technology reveals a number of bottlenecks to be addressed in the face of current challenges.
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。
Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
本文提出了利用协同过滤算法来挖掘客户最可能喜欢的商品项目的方案。
We propose a collaborative filteringCRMalgorithm to mine the most possible commodity item that the customer is most favorable.
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.
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。
This method can improve the traditional collaborative filtering methods, and can solve the single-interest model problem of traditional methods.
实验表明，基于资源语义的协同过滤算法相对于传统协同过滤算法可提高推荐性能。
Experimental results indicate that the algorithm can achieve better prediction accuracy and provide better recommendation results than with the traditional CF algorithms.
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.
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.
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。
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.
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。
Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering.
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.
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.
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。
Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog.
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。
Traditional collaborative filtering does little or no offline computation, and its online computation scales with the number of customers and catalog items.
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.
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.
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".
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。
And point out the problems existed in the traditional collaborative filtering methods: ratings matrix sparsity, cold start, vulnerability, single interest model and scalability issues.
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.
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.
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).
应用推荐