The experiments show that, comparing with the recommendation algorithms based on association rule or on user transaction, the algorithm precision is improved greatly.
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 recommended algorithm of UAPOMR system includes recommendation based on transaction_clusters and recommendation based on association rules clusters.
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。
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。
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。
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。
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。
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。
A algorithm for generating recommendation rule set is proposed based on the users browsing interest, which is measured by considering synth.
提出了一个基于用户浏览兴趣的推荐规则集生成算法，在度量用户浏览兴趣时综合考虑了用户浏览时间和对该页面的访问次数。
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；
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。
We improve current term-based information recommendation algorithm.
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.
对现有基于特征项的推荐算法进行了改进。
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