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.
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。
Realize the clustering algorithm part of the recommendation system based on collaborative filtering and evaluate it.
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.
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.
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。
The collaborative filtering for the personalized recommendation is by far the most widely used and the most successful personalized recommender technology.
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。
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.
为解决协同过滤推荐中“稀疏”和“冷开始”问题，提高推荐精度，提出了基于隐式评分的推荐系统。
Recommendation system based on implicit rating was proposed to improve the precision and solve the problems of "scarcity" and "cold-start".
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。
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.
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.
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.
电子商务系统规模的日益扩大，协同过滤推荐方法也面临诸多挑战：推荐质量、可扩展性、数据稀疏性、冷开始问题等等。
But, with expansion of E-commerce system's size, collaborative filtering approach suffer from many challenges, for instance, quality of recommendations, scalability, sparsity, cold-start problem.
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.
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。
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.
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。
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.
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.
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。
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).
不管用什么方法，协同过滤或基于item相似的推荐都是不会被原谅的商业工具，假阳性般的错误会很快地让用户流失。
Regardless of the method, collaborative filtering or inherent properties of things - recommendations are an unforgiving business, where false positives quickly turn users off.
这意味着，通过收集你是如何与该网站以及与其他用户交往的足够信息，协同过滤（CF ）系统可以为你推荐内容。
What this means is that by collecting enough information on how you interact with the site and with other users, the (CF) system can recommend content to you.
那么，协同过滤和推荐会消失？
一旦内容被推荐至首页，协同过滤系统的工作就算完成了。
Once the content is promoted to the front page, the system's job is done.
推荐系统；协同过滤；用户信任；恶意攻击；相似性。
Recommender System; Collaborative Filtering; User Trust; Malicious Attack; Similarity.
用户评分矩阵稀疏问题影响协同过滤的推荐性能。
The sparse user-item matrix often hurts the performance of recommendation system.
为了有效地解决协同过滤算法中新项目难以推荐的问题，文中提出了一种对项目矩阵进行划分的方法。
To efficiently resolve the problem that the new item is difficult to recommend in collaborative filtering algorithm. In this paper we propose a new method based item matrix partition.
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。
There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods.
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。
This model of collaborative filtering technology is great help in the mitigation of existing sparse problems and recommendation in time.
利用协同过滤来产生推荐，很耗计算。
Using collaborative filtering to generate recommendations is computationally expensive.
我们的算法，也就是商品到商品的协同过滤，符合海量的数据集和产品量，并能实时得到高品质的推荐。
Our algorithm, item-to-item collaborative filtering, scales to massive data sets and produces high-quality recommendations in real time.
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。
User similarity computing plays a very important role in collaborative filtering systems, user recommendation systems as well as social network services.
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。
The experiment results suggested that IAPCF could provide better recommendation results than the traditional item-based collaborative filtering algorithms.
电子商务推荐系统中协同过滤已成为目前应用最广泛、最成功的推荐方法。
In E-commerce recommender system, collaborative filtering technology is the most popular and successful method at present.
应用推荐