别把软件“收藏夹”当鸡肋

我们都知道,浏览器都有"收藏夹"功能,用于收藏常用的网址,便于用户浏览网页,但你是否也知道,许多软件也有"收藏夹"功能呢?用好这些软件的收藏夹,你会觉得非常方便。     

用好注册表中的收藏夹

收藏夹大家应该都很熟悉，在浏览器中使用的最多。它可以把我们常用的一些网页都保存在一起．以后需要使用时只要在收藏夹菜单中打开就可以了。你知道吗。其实注册表里也有收藏夹。我们可以把自己常用的一些分支收藏起来，以后要使用时直接打开就可以了。下面就来介绍一下注册表收藏夹的使用技巧。     

基于用户浏览行为分析的用户兴趣度计算

从用户的浏览行为可以反映用户的兴趣出发,分析了用户的浏览行为与兴趣之间的关系,通过分析把能反映用户兴趣的典型浏览行为归纳为保存页面、打印页面、将页面加入收藏夹、复制页面内容、访问同一页面的次数和在页面上浏览时间这些浏览行为.针对这些浏览行为下的用户兴趣度计算做出阐述,并且在分析基于网页驻留时间和浏览次数计算网页兴趣度的算法的基础上,考虑网页大小的影响因素,使用基于浏览速度计算网页兴趣度,同时利用BP神经网络进行用户兴趣度融合.     

随时随地冲浪用好网络收藏夹

无论你使用什么浏览器,收藏夹的调用还是不变的。面对多地点或者多台电脑的情况下,IE收藏夹又不能同步,是不是很不方便呢？而且当系统崩溃或遇到病毒时,辛苦收藏的网址将面临丢失的危险。既然如此不方便,那么我们还是抛弃IE收藏夹吧。可以通过“网络收藏夹”（网络书签）采替代收藏同步的功能,它是通过将用户收藏的网址保存在网络服务器上实现“网络收藏”功能的,这样就不必担心系统崩溃、中毒等情况导致收藏夹丢失的现象发生了。     

收藏夹“小管家”AM-DeadLink

你的收藏夹中一定收藏了许多喜爱的网站或网页，但是．有时候由于网站网页更新或关闭等原因，造成收藏夹中的部分网址失效。用手工的方法清理是不大可能的事．使用AM-DeadLink这款小巧免费的软件．可以快速查找出收藏夹中的无效网址和重复网址．并对它进行管理。     

百度收藏 让我的网络收藏更实在

上网的时候常常会将常用的网址收藏到浏览器的收藏夹里面,这样可以在下次需要的时候快速调用。但是传统的收藏方式也会出现很多的问题,比如有的网页链接过段时间就失效,收藏链接中网页的内容太多致使打开速度太慢等。而通过网页收藏夹,就可以很好的解决这些问题。     

多系统下的IE收藏夹共享

使用XP和Vista／Win7的朋友可能都有这样一个烦恼,两个或多个Windows系统都装有IE浏览器,都有各自的收藏夹,在一个系统里添加过的网页收藏,想要在另一个系统里访问新收藏的网址时,必须要将收藏夹复制一下才能使用,     

常用网址变导航主页个性义方便

进入常用网页的方法一般有两种,一种是从收藏夹中进入,一种是从网上提供的导航主页中进入。网上有许多著名的导航主页,比如114、2345、360之类就很流行,但它们收藏的网址大多并不是我们所常用的,而我们常用的网址都放在我们的收藏夹中了。如果能把收藏夹中收藏的网址制作成我们自己的导航主页,个性美观且分类更细,效果就非常理想了.     

一键收藏网页到有道笔记

看到有趣的网页总想将其收藏起来，待空闲时再仔细阅读，不过收藏夹越来越庞大，常发生网＆页链接失效导致收藏夹保存的网页无法显示的尴尬。     

让QQ面板包罗万象

大家都知道在QQ的“用户自定义面板”中可以添加一些经常访问的网页,就像使用收藏夹一样,其实除了可以添加网页外,我们还可以添加其他文件夹和网站,这样对于我们的使用操作就方便多了。     

What do web users do? An empirical analysis of web use

This paper provides an empirical characterization of user actions at the web browser. The study is based on an analysis of 4 months of logged client-side data that describes user actions with recent versions of Netscape Navigator. In particular, the logged data allow us to determine the title, URL and time of each page visit, how often they visited each page, how long they spent at each page, the growth and content of bookmark collections, as well as a variety of other aspects of user interaction with the web. The results update and extend prior empirical characterizations of web use. Among the results we show that web page revisitation is a much more prevalent activity than previously reported (approximately 81% of pages have been previously visited by the user), that most pages are visited for a surprisingly short period of time, that users maintain large (and possibly overwhelming) bookmark collections, and that there is a marked lack of commonality in the pages visited by different users. These results have implications for a wide range of web-based tools including the interface features provided by web browsers, the design of caching proxy servers, and the design of efficient web sites.     

蚁群算法在Web站点导航中的应用研究

Web站点导航是Web数据挖掘的一个重要研究领域,是准确理解用户访问网站行为的关键;传统Web站点导航技术很难全面反映出用户对页面浏览的兴趣程度,找到用户感兴趣页面路径准确度比较低;为提高找到用户感兴趣页面路径准确度,提出一种基于蚁群算法的Web站点导航技术;将网络用户看作人工的蚂蚁,用户的浏览兴趣作蚂蚁的信息素,通过利用Web日志数据采用正负反馈机制和路径概率选择机制建立一个Web站点导航模型,挖掘用户感兴趣页面的导航路径;仿真实验结果表明,基于蚁群算法的Web站点导航技术提高了找到用户感兴趣页面路径准确度,更加能够准确反映出用户的浏览兴趣,用于Web站点导航是可行的。     

B-CWB: Bilingual Comparative Web Browser Based on Content-Synchronization and Viewpoint Retrieval

We propose a new way of browsing bilingual web sites through concurrent browsing with automatic similar-content synchronization and viewpoint retrieval facilities. Our prototype browser system is called the Bilingual Comparative Web Browser (B-CWB) and it concurrently presents bilingual web pages in a way that enables their contents to be automatically synchronized. The B-CWB allows users to browse multiple web news sites concurrently and compare their viewpoint of news articles written in different languages (English and Japanese). Our viewpoint retrieval is based on similar and different detection. We described categorizing pages in terms of viewpoint: the entire similarity, the content difference, and subject difference. Content synchronization means that user operation (scrolling or clicking) on one web page does not necessarily invoke the same operations on the other web page to preserve similarity of content between the multiple web pages. For example, scrolling a web page may invoke passage-level viewpoint retrieval on the other web page. Clicking a web page (and obtaining a new web page) invokes page-level viewpoint retrieval within the other site's pages through the use of an English-Japanese dictionary.     

基于学习自动机和用户兴趣的PageRank算法研究

针对传统PageRank算法存在的平分链接权重和忽略用户兴趣等问题,提出一种基于学习自动机和用户兴趣的页面排序算法LUPR。在所提方法中,给每个网页分配学习自动机,其功能是确定网页之间超链接的权重。通过对用户行为进一步分析,以用户的浏览行为衡量用户对网页的兴趣度,从而获得兴趣度因子。该算法根据网页间的超链接和用户对网页的兴趣度衡量网页权重计算每个网页的排名。最后的仿真实验表明,较传统的PageRank算法和WPR算法,改进后的LUPR算法在一定程度上提高了信息检索的准确度和用户满意度。     

基于用户兴趣分析的网页生命周期建模

网页在其生命周期内的活跃程度会随时间发生变化。有的网页只在特定的阶段有价值,此后就会过时。从用户的角度对网页的生命周期进行分析可以提高网络爬虫和搜索引擎的性能,改善网络广告的效果。利用一台代理服务器收集的网页访问量信息,我们对网页的生命周期进行了研究,给出了用户兴趣演变的模型。这个模型有助于更好地理解网络的组织与运行机理。     

Automated Semantic Analysis of Schematic Data

Content in numerous Web data sources, designed primarily for human consumption, are not directly amenable to machine processing. Automated semantic analysis of such content facilitates their transformation into machine-processable and richly structured semantically annotated data. This paper describes a learning-based technique for semantic analysis of schematic data which are characterized by being template-generated from backend databases. Starting with a seed set of hand-labeled instances of semantic concepts in a set of Web pages, the technique learns statistical models of these concepts using light-weight content features. These models direct the annotation of diverse Web pages possessing similar content semantics. The principles behind the technique find application in information retrieval and extraction problems. Focused Web browsing activities require only selective fragments of particular Web pages but are often performed using bookmarks which fetch the contents of the entire page. This results in information overload for users of constrained interaction modality devices such as small-screen handheld devices. Fine-grained information extraction from Web pages, which are typically performed using page specific and syntactic expressions known as wrappers, suffer from lack of scalability and robustness. We report on the application of our technique in developing semantic bookmarks for retrieving targeted browsing content and semantic wrappers for robust and scalable information extraction from Web pages sharing a semantic domain. This work has been conducted while the author was at Stony Brook University.     

基于Web日志的煤矿企业网站个性化推荐服务研究

为了解决煤矿企业网站用户查找信息难的问题,提出了一种基于Web日志的煤矿企业网站个性化推荐服务模型。该模型应用关联规则对新用户进行页面推荐,应用聚类算法对老用户进行页面推荐;并结合点击网页的次数、网页的浏览时间、雅可系数与最长公共路径系数来度量用户兴趣度的方法,可为用户准确地推荐其感兴趣的页面。测试结果表明,该模型能够有效地对网页资源进行分类并进行个性化推荐。     

用户行为驱动的网页布局自动调整的研究

在传统的Web网站中,网页的布局往往由网页制作人员安排并很少变化.为了更好的为网络用户提供服务,提出通过对Web日志的数据清洗,识别出每个用户在一个会话期内访问的页面,依据网页内客在逻辑上的关系和用户经常访问的页面,得到用户对网页内容的兴趣度矩阵及各子项目的兴趣度矩阵.对网络用户根据兴趣度短阵进行层次化的分类,得到每个...     

Navigating the applet-browser divide

The World Wide Web has emerged as a new application-delivery platform. In response, developers are offering users sophisticated Web-based Java applets that range from cybershopping carts to complex tools for genome mapping. These applets give you application functionality without taking up space on your hard drive. But trailing behind the applet bounty are new usability questions. A major one is how to make applet navigation seamless in the Web browser domain. Java applets are programs you write in Java and integrate into your Web page. Although applets can provide functionality similar to traditional applications, the applet code need not be installed on the users' hard drive. Instead, the applets execute Java-compatible Web browsers. Unlike standard Web pages, which users simply visit and browse, applet-enhanced pages let the user manipulate applet components and dynamically interact with information. The author discusses tools, techniques and concepts to optimize user interfaces     

利用ε-贪婪学习和用户行为反馈的搜索引擎网页排序算法

为了提高网页排序的准确性,提出一种基于ε-贪婪学习和用户点击行为的网页排序算法。首先,根据用户查询,通过轮盘赌策略向用户推荐相关网页列表;然后,根据用户点击网页的行为进行ε-贪婪学习,计算得到排序系统中的强化信号,通过奖励和惩罚机制为每个网页计算相关性程度值;最后,根据相关性程度对网页进行重新排序。随着用户反馈的信息越来越多,相关网页会排列在列表的最高等级上。实验结果表明,提出的算法能够准确地推荐出相关网页,在P@n、NDCG和MAP性能指标上都获得了较优的性能。