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。
The result of mining shows that, in the case of the data extremely sparseness, project-based collaborative filtering recommendation method is effective to improve the recommended quality.
挖掘结果表明，在数据极端稀疏的情况下，基于项目的协同过滤推荐方法明显的提高了推荐质量。
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。
As you can see from above, it is certainly possible to have a good collaborative filtering system without a recommendation engine (as seen in Flickr).
正如你从上述中所看到的，如果没有一个推荐引擎(如看到的Flickr)这当然也有可能是一个良好的协同过滤系统。
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。
There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods.
解决推荐问题有三个通常的途径：传统的协同过滤，聚类模型，以及基于搜索的方法。
This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time.
该模型的建立对于缓解协同过滤技术中存在的稀疏性问题、推荐的实时性问题有很大的帮助。
Collaborative Filtering (CF) is used for forming recommendation by analyzing the common "taste" Shared by a group of customers.
协同过滤技术可以通过分析客户群共同的消费品味来形成推荐。
Compared traditional collaborative filtering method, the experimental results show that our approach can find a solution to the problem of new item recommendation effectively.
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。
User similarity computing plays a very important role in collaborative filtering systems, user recommendation systems as well as social network services.
用户相似度计算在协同过滤系统、用户推荐系统以及社交网络中有着非常重要的作用。
The collaborative filtering for the personalized recommendation is by far the most widely used and the most successful personalized recommender technology.
其中，个性化推荐系统中的协同过滤推荐是迄今为止应用最广泛、最成功的推荐技术。
Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms.
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。
The experiment results suggested that IAPCF could provide better recommendation results than the traditional item-based collaborative filtering algorithms.
实验结果表明，IAPCF算法比传统的基于项目的协同过滤算法具有更好的推荐精度。
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。
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。
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。
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。
Be inspired by the research achievement in e-commerce fields, we try to introduce the collaborative filtering technology into research of personalized recommendation of learning resources.
受电子商务研究领域中相关研究成果启发，我们尝试将协同过滤推荐技术引入学习资源的个性化推荐研究中。
Collaborative filtering is the most widely used and successful technology for personalized recommender systems. However it faces challenges of scalability and recommendation accuracy.
协同过滤是个性化推荐系统中应用最广泛和最成功的推荐技术，但是它也面临着推荐准确度和可扩展性两大挑战。
However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items.
但是随着用户数量和系统规模的不断扩大，协同过滤推荐技术将面临严重的数据稀疏性、超高维、冷启动和实时推荐等方面的挑战。
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 is frequently used in solving information overload problem, Collaborative Filtering is a main tool used in Personalized Recommendation.
协同过滤是经常被采用的解决信息过载问题的方法，是个性化推荐的主要方法之一。
Collaborative Filtering is frequently used in solving information overload problem, Collaborative Filtering is a main tool used in Personalized Recommendation.
协同过滤是经常被采用的解决信息过载问题的方法，是个性化推荐的主要方法之一。
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