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。
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。
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.
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。
Amazon and Netflix, a site that offers films for hire, use a statistical technique called collaborative filtering to make recommendations to users based on what other users like.
Amazon和Netflix，一家提供影片出租的网站，使用一种被称为协作式过滤的统计技术，按照其他用户的喜好为新用户提供建议。
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。
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。
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.
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。
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.
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。
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。
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 can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability.
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。
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.
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。
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.
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。
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。
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；
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.
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。
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.
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。
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.
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。
Thispaper introduces two main filtering methods, named content-based and collaborative filtering;
本文介绍了基于内容和基于协作的两种不同的过滤方法；
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;
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；
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