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.
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.
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.
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.
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.
但由于传统的协同过滤算法没有考虑项目多内容问题，存在项目多内容情况时推荐质量较差。
Unfortunately, traditional collaborative filtering algorithm does not consider the problem of item's multiple contents and often leads to bad recommendation when item has multiple contents.
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.
基于此不足，在用户聚类协同过滤算法的基础上进行了改进，其基本思想是在基于用户聚类的基础上研究用户多兴趣的表示。
To address this problem, a collaborative filtering based on user clustering strategies to improve the basic idea is the basis of user-based clustering of users and more interested in that.
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。
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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。
To address these problems, a collaborative filtering recommendation algorithm combining probabilistic relational models and user grade (PRM-UG-CF) is presented.
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；
This paper puts forward a collaborative filtering algorithm based on rough set and fuzzy clustering which automatically fills vacant ratings through rough set theory.
对在此基础上生成的模范用户模型应用协同过滤推荐算法，目标用户在线推荐的效率有很大的提高，模范用户模型相对稳定，推荐精度也有所改善。
Collaborative filtering algorithm based on model users greatly improves the efficiency of online recommendation, makes model users relatively stable and also improves the accuracy of recommendation.
提出一种基于信任机制的协同过滤推荐算法，其中，直接信任度基于共同评价项目得出，推荐信任度通过对项目的预测得出。
This paper proposes a collaborative filtering recommendation algorithm based on trust mechanism. Direct trust is based on common rating data and indirect trust is based on the predict data.
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.
本文的主要特色：把基于内容过滤的推荐算法和协同过滤的推荐算法相结合；
The main characteristics: the recommendation algorithm-based content filtering and collaborative filtering algorithm combined with the recommendation;
本文的主要特色：把基于内容过滤的推荐算法和协同过滤的推荐算法相结合；
The main characteristics: the recommendation algorithm-based content filtering and collaborative filtering algorithm combined with the recommendation;
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