此外，Neo4j还提供了非常快的图形算法、推荐系统和OLAP风格的分析，而这一切在目前的RDBMS系统中都是无法实现的。
This gives secondary effects like very fast graph algos, recommender systems and OLAP-style analytics that are currently not possible with normal RDBMS setups.
此方法的目的在于对推荐系统执行用户调研或者在线评估之前过滤掉性能较差的算法。
The goal of this approach is to filter out algorithms which have poor performance before the recommender system will be evaluated with a user study or online type evaluations.
可能会存在推荐系统潜在可以推荐的项目，但是算法不包括这些项目。
There might be items for which the system can potentially make a recommendation, but the algorithm never suggests those items.
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.
但是，随着推荐系统的广泛应用，推荐算法的安全问题日益显现。
While, with the recommend systems widely used, the recommendation algorithm's security problem is increasingly appear.
本系统给出了基于关联规则挖掘和基于用户事务模式聚类两种推荐算法。
The system gives two kinds of recommendation algorithms based on association rule mining and user's transaction pattern clustering.
智能信息推荐系统能够通过用户偏好，利用信息过滤算法主动剔除无关信息。
Intelligent information recommending systems can kick out user-useless information using filtering algorithms and user's profile.
本文详细地介绍了UAPOMR系统的每一个模块实现的理论基础，并通过实验对提出的推荐算法进行了评估。
We introduce the whole design of UAPOMR system comprehensively and in detail, and evaluate the recommended algorithm by experiments.
UAPOMR系统的推荐算法包括基于事务聚类的推荐和基于关联规则聚类的推荐。
The recommended algorithm of UAPOMR system includes recommendation based on transaction_clusters and recommendation based on association rules clusters.
据此，我们推荐了倾斜高斯烟流模式及其参数算法系统予测排气塔后部的浓度分布。
Further more, tilted model of Gaussian plume and parameter system have been recommended to predict concentration distribution behind wind tower.
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.
本文提出一种资源自适应的推荐算法，使系统在推荐效果和系统性能之间取得了动态平衡。
This paper proposes a resource-adaptive algorithm to address this problem, which tries to balance the precision and the efficiency of the system.
实验结果表明，该算法可以有效地提高数字图书馆个性化推荐系统的可扩展性及推荐准确度。
The experimental results demonstrate that the algorithm can effectively improve scalability and accuracy of the digital library of personalized recommendation system.
在实际的推荐系统中，在线推荐的时间复杂度是衡量推荐算法的主要指标。
The time complexity of online recommendation was the key indicator to measure recommend algorithm in real recommendation system.
推荐算法的好坏直接影响推荐系统的效率。
The fair or foul of recommendation algorithm can directly affect the recommendation system's efficiency.
实验结果表明，本文提出的算法能很好的发现微博用户的兴趣，提高推荐系统的质量。
Experimental results indicate the algorithm we proposed performs better in detecting weibo user interest and enhances the quality of the recommendation system.
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.
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.
本文提出的算法能够有效缓解数据稀疏性问题，提高推荐系统的推荐质量。
The improved methods can effectively alleviate the problem of sparsity and improve the quality of recommendation system.
新闻推荐规则是建立在用户特征提取算法的基础之上实现的，是对本系统实现的补充和完善。
The news recommendation rule generation part is based on feature extraction part and it is the complement of the system.
针对网络新闻推荐系统推荐准确率偏低的问题，提出一种基于多主题追踪的网络新闻推荐算法。
A Web news recommendation method based on multiple topic tracking was proposed to improve the precision of recommendation.
我们在对本音乐系统进行了用户满意度的主观测试以及与国内同类音乐系统新浪和搜狐的比较后，证明依照本文所述算法和系统建模所实现的音乐推荐系统确有独到之处。
We test the user-satisfaction with the subjective test, as well as domestic similar music suggestion system Sina and Sohu. The results suggest that our music suggestion system was predominant.
我们在对本音乐系统进行了用户满意度的主观测试以及与国内同类音乐系统新浪和搜狐的比较后，证明依照本文所述算法和系统建模所实现的音乐推荐系统确有独到之处。
We test the user-satisfaction with the subjective test, as well as domestic similar music suggestion system Sina and Sohu. The results suggest that our music suggestion system was predominant.
应用推荐