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。
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。
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 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。
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。
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。
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