数字图书馆网络服务的监测   总被引：2，自引：0，他引：2  

庄纪林 《大学图书馆学报》2008,26(3):38-42,45

数字图书馆通常是一个分布式的松散耦合的多系统构成的庞大的应用服务体系,采用"安装代理"和"远程登录"的商业化网络服务监测软件并不适用于数字图书馆网络服务的监测。在分析数字图书馆网络服务特点的基础上提出了"基于内容"的数字图书馆网络服务监测方案,解决了数字图书馆网络服务体系中只有"访问权限"而没有"管理权限"的网络服务的监测问题。  相似文献   

JPEG 2000在数字图书馆的应用   总被引：1，自引：0，他引：1  

王翠英 《新世纪图书馆》2004,(5):27-28

论文介绍了JPEG2000图像压缩标准的特点及其在数字图书馆图像压缩、基于内容的检索、非线性视频编辑等方面的应用。  相似文献   

Scale and Translation Invariant Collaborative Filtering Systems   总被引：1，自引：0，他引：1  

Daniel Lemire 《Information Retrieval》2005,8(1):129-150

Collaborative filtering systems are prediction algorithms over sparse data sets of user preferences. We modify a wide range of state-of-the-art collaborative filtering systems to make them scale and translation invariant and generally improve their accuracy without increasing their computational cost. Using the EachMovie and the Jester data sets, we show that learning-free constant time scale and translation invariant schemes outperforms other learning-free constant time schemes by at least 3% and perform as well as expensive memory-based schemes (within 4%). Over the Jester data set, we show that a scale and translation invariant Eigentaste algorithm outperforms Eigentaste 2.0 by 20%. These results suggest that scale and translation invariance is a desirable property.  相似文献   

A Novel Approach for Enhancing Lifelong Learning Systems by Using Hybrid Recommender System     

Ahmad  A.  Kardan Omid  R.  B.  Speily Somayyeh  Modaberi 《美中教育评论》2011,(4):482-491

  相似文献   

推荐系统在高校数字图书馆的应用研究   总被引：5，自引：0，他引：5  

谢琳惠 《现代情报》2006,26(11):72-74

信息时代，数字图书馆连渐成为满足图书馆用户个性化服务的主要解决方案。本文在描述高校数字图书馆个性化服务的概念及其特点后，提出使用电子商务中的推荐系统采帮助实现高校数字图书馆的个性化服务的论点。文章中建立了高校数字图书馆推荐系统的框架模型．井在此基础上对高校数字图书馆推荐系统的组成、处理、以及推荐结果的形式进行了探讨，指出了适合本系统的推荐算法。  相似文献   

数字图书馆中主动信息过滤系统的构建研究   总被引：6，自引：0，他引：6       下载免费PDF全文

焦玉英  王娜 《中国图书馆学报》2007,33(4)

设计了一个结合使用协作过滤和基于内容过滤的主动信息过滤的实验系统。其结构框架的主要部分有:智能代理、检索服务器、用户需求文档数据库、过滤服务器、结果处理器和推送服务器。它采用机器学习的机制来预测用户新的兴趣。  相似文献   

一种面向高校图书馆的个性化图书推荐系统     

王连喜 《现代情报》2015,35(12):41-46

个性化图书推荐主要是以用户特征和借阅行为为挖掘对象,通过获取用户的兴趣特征及隐含的需求模式,实现用户与图书相互关联的个性化图书推荐服务。本文通过挖掘用户的背景信息构建用户特征模型,然后在设计喜好值计算、用户相似度计算和内容相似度计算以及标签信息获取方法的基础上,研究多种不同的图书推荐方法,以挖掘用户的潜在信息需求。最后利用图书馆的真实数据设计面向高校图书馆的个性化图书推荐系统,同时以标准网络数据集通过实验验证来评估推荐方法的有效性。  相似文献   

基于高斯混合模型的图像检索算法研究     

肖维 《人天科学研究》2011,10(4):48-50

通过研究聚类算法在图像处理上的应用,提出了一种基于高斯混合模型聚类的图像检索方法。该检索方法首先提取每幅图像的特征,并以特征值为数据集建立高斯混合模型,得到所有图像的高斯混合模型。再以所有图像的混合模型参数集作为数据集,用基于高斯混合模型的聚类算法进行聚类。最后输出检索例图所在的类,即得到检索结果。  相似文献   

A collaborative filtering similarity measure based on singularities     

Jesús Bobadilla  Fernando OrtegaAntonio Hernando 《Information processing & management》2012

Recommender systems play an important role in reducing the negative impact of information overload on those websites where users have the possibility of voting for their preferences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to calculate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.  相似文献   

Δ-distance: A family of dissimilarity metrics between images represented by multi-level feature vectors     

Marta Rukoz  Maude Manouvrier  Geneviève Jomier 《Information Retrieval》2006,9(6):633-655

This article presents the Δ-distance, a family of distances between images recursively decomposed into segments and represented by multi-level feature vectors. Such a structure is a quad, a quin or a nona-tree resulting from a fixed and arbitrary image partition or from an image segmentation process. It handles positional information of image features (e.g. color, texture or shape). Δ-distance is the generalized form of dissimilarity measures between multi-level feature vectors. Using different weights on tree nodes and different distances between nodes, distances between trees or visual similarity between images can be computed based on the general definition of Δ. In this article, we present three Δ-based distance families: two families of distances between tree structures, called -distance( for Tree) and -distance ( for Segment), and a family of visual distances between images, called ( for Visual). The -distance visually compares two images using their tree representation and the other two distances compare the tree structures resulting from image segmentation. Moreover, we show how existing distances between multi-level feature vectors appear to be particular cases of the Δ-distance  相似文献