个性化推荐系统在这样的背景下应运而生。
Personalized Recommender Systems emerge under the background of this, which becomes the research focus in the domestic and overseas.
协同推荐技术是实现个性化推荐系统的一种有效方法。
So this paper presents the recommendation system into the E-learning platform, in order to accomplish the purpose of personalized service.
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.
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.
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.
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。
The experimental results demonstrate that the algorithm can effectively improve scalability and accuracy of the digital library of personalized recommendation system.
个性化推荐系统已成为图书馆提供个性化服务的重要手段，而用户模型则是个性化推荐系统的基础和核心。
Personalized recommending system is one of the important means providing individual information in library . User profile is the basis and core of the personalized recommending system.
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。
Collaborative filtering is the most widely used and successful technology for personalized recommender systems. However it faces challenges of scalability and recommendation accuracy.
个性化推荐技术是电子商务系统中重要的技术，但对一般的非商务型网站如何向用户提供推荐服务成为当前研究的热点。
Personalization recommendation technique is an important technology in E_commerce, but how to provide recommendation service in net site which is not E_commerce is becoming a hot research now.
本文提出了基于数量化i类理论的人工心理模型的建模方法，并介绍了该模型在个性化商品推荐系统中的应用。
We propose a modeling method of artificial psychology which based on Quantification Theory I and introduce how to apply it in recommender system in this paper.
他的研究兴趣包括推荐系统、个性化、数据挖掘以及人工智能。
His research interests include recommendation systems, personalization, data mining, and artificial intelligence.
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.
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.
提出了一种多维关联规则推荐系统，为客户提供更加准确的个性化推荐。
This paper presents a multidimensional association rules recommendation system which provides more exact personalization recommendation for customer.
系统可借助这种动态结构，向不同的用户群推荐适合的预送页面序列，逐步达到个性化服务的目标。
Such kind of dynamic structure might be perfect step by step and make web stations arrive at the aim: offering personalized service for their users.
本文设计出了基于范例推理的个性化旅游推荐系统，并详细研究了系统中CBR关键技术的实现方案。
This paper design the recommendation of individual tourism plan system based on CBR and research the key technique and implement scheme of CBR.
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.
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。
Collaborative filtering is thriving among lots of personalized recommendation technology which leads the recommendation system trends of major e-commerce platforms.
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。
Personalized Garment Pattern system is a clothing recommendation system based on interactive and user preferences.
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。
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。
At last, it demonstrates the Web personalized recommendation and shows the results.
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