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。
This paper describes the realization process of the personalized information recommendation in theory.
在上述工作的基础上，设计并实现了基于兴趣模型的个性化信息推荐系统。
On the ground of above work, we designed and implemented a personalized information recommendation system of interest-based model.
完成以下工作：1分析比较各种个性化信息推荐系统，尝试的构建一个性化信息推荐系统。
The following works have been mostly completed:1 The paper analyses and compares kinds of personalized information services recommender system.
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。
Finally, studies on personalized information recommendation based on social tagging are analyzed, and find matrix, clustering and network analysis are three primarily methods.
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。
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.
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。
The research of personalized Active information Service has made a big progress during these years, the most important of which is personalized information recommendation.
通过对用户浏览行为的捕获，形成用户近期视图反应用户的这种近期兴趣变化，以此为用户提供及时准确的个性化信息推荐。
It catches the user of browser behavior, formationing user current interest view to respond the user of current changes and providing the recommendation.
2009年的互联网上充斥着海量信息，在这个年代，个性化意味着提供高效的过滤器和内容推荐 。
With the glut of information on the Web circa 2009, personalization in this era means providing effective filters and recommendations .
为了缓解信息过载的压力，提升消费者的满意度，个性化推荐服务应运而生。
In order to alleviate the pressure of information overloading and enhance consumer satisfaction, personalization recommendation service came into being.
在信息过滤改进模型指导下，本文提出并构建了一个电子商务个性化推荐系统。
A personalized recommending system of electronic commerce has been designed on the improved information filtering model.
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。
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.
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。
The proposed idea and technique can also be used for other personalized information recommendation systems on the Internet.
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.
个性化推荐是一个有效的减轻用户的负担，对信息检索的方法。
Personalization recommendation is a valid method for lightening the user's burden on information retrieval.
信息过载问题的出现，为个性化推荐系统提供了新的挑战。
A new challenge to personalized recommendation is provided when problem of system information overload appears.
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。
Personalized recommendation system (hereinafter referred to as PRS) applied to the fields of e-commerce and information services early, and has been relative mature.
学员端应用：主要是学员网上预订借、系统个性化快速推荐和借阅信息维护管理。
Trainees applications: mainly refers to trainees 'borrowing in advance via the net, the efficient recommendation by the system and the maintenance management of the borrowing information.
图书馆的个性化信息服务可以分为个性化信息定制服务和个性化信息主动推荐服务两种类型。
In a library, individuation information service includes individuation custom information service and individuation active recommendation information service.
个性化信息服务质量的高低不仅仅取决于具体的检索技术、推荐技术，还取决于用户建模技术，而后者尤其重要。
The quality of PIS is dependent on not only the technology of retrieval and recommend, but also the technology of user modeling, especially the latter is more important.
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.
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。
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。
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.
应用推荐