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 recommendation algorithm is one of the most successful technologies in thee-commerce 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。
To address these problems, a collaborative filtering recommendation algorithm combining probabilistic relational models and user grade (PRM-UG-CF) is presented.
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。
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。
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。
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。
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.
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。
Collaborative filtering, which is widely used recommendation algorithm, usually provides predicted ratings as recommendation.
协作过滤是应用最为广泛的推荐技术，通常提供预测评分作为推荐。
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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