Personalized recommendation - recommend things based on the individual's past behavior.
个性化的推荐：基于用户过去的行为作出推荐。
At last, it demonstrates the Web personalized recommendation and shows the results.
最后本文通过实验模拟了个性化内容推荐系统的运行结果。
So this recommendation is also a Personalized recommendation, since it is based on my past behavior.
所以，这也是一个个性化推荐，因为它是基于我过去的行为的。
A new challenge to personalized recommendation is provided when problem of system information overload appears.
信息过载问题的出现，为个性化推荐系统提供了新的挑战。
Satnam : A good personalized recommendation system can mean the difference between a successful and a failed website.
一个优秀的个性化的推荐搜索引擎可以说是网站的成功和失败的分水岭。
Therefore the research of the personalized recommendation model has higher academic value and application prospect.
因此，个性化推荐技术的研究，具有较高的学术价值和应用前景。
Therefore, the personalized recommendation technology in the field of mobile search application is even more important.
因此，个性化推荐技术在移动搜索领域的应用则更加重要。
Also note that this recommendation is also a Personalized recommendation, since it is based on an item that I clicked recently.
同时，它也是一项个性化推荐，因为推荐基于我最近点击的一个物品。
Experimental results on real world data show that this method is an effective method that can achieve personalized recommendation for different customers.
实数据上的实验表明该方法是一种有效的能为不同客户产生准确而个性化的商品推荐方法。
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 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 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。
Personalized recommendation system (hereinafter referred to as PRS) applied to the fields of e-commerce and information services early, and has been relative mature.
个性化推荐系统(简称prs)最早应用于电子商务和信息服务领域，现已相对成熟。
The experimental results demonstrate that the algorithm can effectively improve scalability and accuracy of the digital library of personalized recommendation system.
实验结果表明，该算法可以有效地提高数字图书馆个性化推荐系统的可扩展性及推荐准确度。
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。
At present, there are mainly two ways to implement digital library personalized services based on contextual model, that is, through personalized retrieval and personalized recommendation.
目前，基于情景模型的数字图书馆个性化服务主要通过个性化检索和个性化推荐两种方式实现。
Based on the analysis of the user profile and the characteristics of personalized document recommendation, this paper proposes a personalized recommendation method based on semantic expansion.
本文在分析用户文档和文献个性化推荐特点的基础上，提出了一个基于语义扩展的个性化推荐方法。
Compared with the traditional personalized recommendation model, this one had higher speed and better accuracy, which provided important guiding value for many personalized information servers.
该模型较传统的个性化推荐在的速度和准确性上都有较大的改善，应用领域广泛，为个性化信息服务的提供者提供了很好的参考价值。
This paper discusses current user modeling techniques and the ability of corresponding personalized recommendation. It puts forward a new compositive user model to develop these models 'advantages.
本文在讨论各种现有用户建模技术及相应的个性化信息推荐方式的基础上，给出一种新型的综合用户建模方法。
Finally, studies on personalized information recommendation based on social tagging are analyzed, and find matrix, clustering and network analysis are three primarily methods.
最后，分析了社会化标注中个性化信息推荐的研究，发现借助矩阵、聚类和网络的分析是三种主要思路。
The proposed idea and technique can also be used for other personalized information recommendation systems on the Internet.
所述的设计思想和技术也适用于其它互联网个性化信息自动推荐系统。
Recommendation algorithms provide an effective form of targeted marketing by creating a personalized shopping experience for each customer.
通过为每位顾客建立个性化的购物体验，推荐算法提供了一种有效的定向营销形式。
Based on the introduction of the meaning and structure of social tagging, this paper mainly discusses the advancements of personalized information recommendation based on social tagging.
本文在对社会化标注的内涵和结构加以简单介绍的基础上，重点探讨了基于社会化标注进行推荐的相关进展。
The research of personalized Active information Service has made a big progress during these years, the most important of which is personalized information recommendation.
近年来，个性化主动信息服务的研究取得了很大的进展。而在个性化主动信息服务中最重要的服务就是个性化信息推荐。
Personalized Garment Pattern system is a clothing recommendation system based on interactive and user preferences.
个性化服装款式系统是基于交互和用户偏好的服装推荐系统。
This paper describes the realization process of the personalized information recommendation in theory.
本文从实现的原理上对该个性化信息推荐方式进行了理论探讨。
The personalized services realize the purpose of initiative recommendation by collecting and analyzing the information of customers and study the interests and behavior of them.
个性化服务通过收集和分析用户信息来学习用户的兴趣和行为，从而实现主动推荐的目的，为不同用户提供不同的服务，以满足不同的需求。
ALIRS also supplies the personalized services such as key information extraction and valuable information recommendation.
提供关键信息抽取、有价值信息自动推荐等个性化服务。
ALIRS also supplies the personalized services such as key information extraction and valuable information recommendation.
提供关键信息抽取、有价值信息自动推荐等个性化服务。
应用推荐