对现有基于特征项的推荐算法进行了改进。
We improve current term-based information recommendation algorithm.
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.
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.
电子商务推荐算法经常要运行在一个充满挑战的环境里。
E-commerce recommendation algorithms often operate in a challenging environment.
该推荐算法可以用来使滚切大、小齿轮轮齿的机床尺寸减小。
The proposed algorithm makes it possible to decrease the dimensions of machines that are used for gear and pinion tooth generation.
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.
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.
最后根据餐饮的特点，作者提出了自己的一些关于推荐算法的想法。
Finally according to the characteristics of the food and beverage, the author put forward some ideas about the recommendation algorithm.
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.
对于非常大的数据集，一个可扩展的推荐算法必须离线运行最昂贵的计算。
For very large data sets, a scalable recommendation algorithm must perform the most expensive calculations offline.
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.
未来，我们期望零售业为定向营销更广泛地应用推荐算法，包括网上和网下。
In the future, we expect the retail industry to more broadly apply recommendation algorithms for targeted marketing, both online and offline.
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;
通过为每位顾客建立个性化的购物体验，推荐算法提供了一种有效的定向营销形式。
Recommendation algorithms provide an effective form of targeted marketing by creating a personalized shopping experience for each customer.
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.
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.
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：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.
实验表明，该算法比使用基于关联规则和基于用户事务的推荐算法的精确性有较大幅度的提高。
The experiments show that, comparing with the recommendation algorithms based on association rule or on user transaction, the algorithm precision is improved greatly.
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.
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.
实验证明，本文提出的这种算法在预测精度上较传统的推荐算法和没有引入兴趣方差的推荐算法有很大的提高。
It proves that this method can obtain a better predictive precision, compared with traditional recommendation algorithm and algorithm which don't take into account interest variance.
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.
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.
基于多主题追踪的推荐算法采用多个用户模型表示用户对不同主题的兴趣，并动态更新用户模型以动态反映用户的兴趣变化。
The proposed algorithm used multiple user profiles to represent user's interests in different topics, and dynamically updated user's profile to reflect the changing of user's interests.
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.
谷歌则在它的搜索算法里加入了社交媒体成分，使用户可以将搜索结果推荐给自己的朋友。
GOOGLE, FOR ITS PART is offering a search algorithm with a social media component that allows users to recommend searches to their friends.
应用推荐