移动终端Web页面的优化处理研究

移动终端的多样性及用户的个性化要求是传统Web页面遇到的挑战。为获得与电脑相一致的浏览效果,提出一个服务器端的Web页面自适应方法,通过优化系统处理生成自适应页面。该方法通过用户为设备设置Web页面上项的优先级,系统对页面项进行重新排序、显示和删除处理。测试结果表明,系统及自适应页面具有可用性。     

基于HTML5的响应式Web页面重组适配技术研究

在网页浏览和网站访问使用上,移动终端与PC机终端相比,有显示屏幕大小不统一,页面布局不适应,噪声信息较多等问题,网络上海量的网站在移动终端访问时需将网页信息进行适配和重组。HTML5标准提供的新特性新标签可以更好地适应现今多终端访问的需求,采用响应式原则设计的页面在移动终端访问时也能提供给用户更好的服务和体验。本文提出一种方法,将已有的网站和网页通过前台框架技术进行Web页面的重组和适配,从而转换成基于HTML5新标准的响应式页面,实现了基于HTML5的响应式Web页面重组。网站可以更好地适应多终端访问,为用户提供更好的服务。     

基于移动终端的系统登录方法的研究与开发

本文针对在不安全的网络环境或不安全的上网系统的环境下,提出一种基于移动终端的安全登录系统,克服了现有移动登录方案的不足。当用户使用浏览器在一个Web信息系统或应用系统进行登录操作时,Web信息系统或应用系统将浏览器与Web系统之间的会话标识数据以二维码的形式显示在浏览器上,用户只需使用移动终端的摄像头扫描二维码获得会话标识数据并利用移动终端完成身份认证即可实现用户浏览器在Web信息系统或应用系统的安全登录,用户无需在计算机上输入帐户名、口令,从而避免了在公共计算机上输入帐户名、口令导致的帐户名、口令被窃取的风险,而且将用户在不同Web信息系统或应用系统的身份凭证保存在移动终端中也给用户进行登录操作带来方便。     

基于WebKit的一种渲染改进方法

移动浏览器是移动互联网产业界关键的竞争之地。在分析WebKit浏览器引擎的页面解析、渲染过程和方法的基础上,提出一种针对WebKit渲染机制的改进方法。该方法依据浏览局部性原理,改变对页面元素的渲染顺序,优先渲染ViewPort范围内的页面元素。这对于硬件有限的移动终端,大大减少了用户浏览复杂页面时的等待时间。实验表明,该方法有效地提高了移动终端Web页面显示速度,改善了用户体验。     

在线新闻系统移动客户端技术研究

随着智能手机等移动终端的普及,越来越多的人喜欢用手机上网,这样可以跨越时间、地点的限制。为了给移动终端用户更好的使用体验,嘉兴日报报业传媒集团自主设计、开发了新闻系统移动客户端,自动分析、抓取嘉兴在线新闻网站中多个栏目下的新闻页面,保留新闻内容,下载新闻图片并自动生成小图、中图,去除广告等冗余信息,自动生成适合在移动终端查看的Web页面文件。苹果系统和安卓系统智能手机直接快速高效地浏览嘉兴在线网站的新闻资讯。     

基于网页格局的内容分块算法

随着移动上网业务的日益发展,人们迫切希望能够通过手持终端设备访问丰富的Web内容。同时,由于手持终端设备存在着多方面的局限性,使得必须对所要访问的Web页面进行转换处理。本文提出了一种新的内容分块算法,能够智能化地通过分析内容关系对Web页面信息进行分块和抽取,使得手持终端设备用户能够快速、高效地访问Web内容。     

面向移动终端的Web页面重组技术综述*

在移动终端上浏览传统Web页面,存在着页面布局不合理、屏幕适应性差、噪声信息多等问题,严重影响页面的显示效果.Web页面重组技术通过对页面信息进行提取、组合,能够有效地解决上述问题,能够满足移动用户丰富多彩的页面体验效果.首先从页面提取和组合等方面对页面重组技术进行了论述,同时分析了相关技术的适用范围以及其复杂性,最后对当前领域研究的重点问题进行了总结.     

移动Web平台植物辨识系统设计与实现

基于移动Web平台,以页面动态Ajax技术为基础,设计与实现了在线植物信息辨识训练系统.移动终端体积小、成本低,有助于广大业余及专业爱好者随时进行植物认知学习.     

基于示例的Web信息自动获取系统的设计与实现

介绍了一个基于多层体系结构的个性化Web信息自动获取系统的设计与实现,提出了一种新的基于少量中文示例Web页面的兴趣特征抽取算法,并给出了系统的检全率和检准率测试结果.实验结果表明,较基于关键词的搜索引擎而言,该系统能充分考虑用户的兴趣偏好(示例),长期、主动地向用户提供更加准确的Web信息获取服务.     

Web页面信息主动检索模型

单个页面信息量远远大于特定用户对页面中的信息需求.为快速准确从当前页面中获取特定用户所需求的兴趣信息,提出了页面信息主动检索模型.该检索模型中,根据页面Block特点将当前Web页面转化成信息树,根据用户过去的浏览行为构造用户特征树,挖掘用户特征树产生用户需求信息集,然后从当前页面中检索需求的信息,获取用户兴趣信息集.详述了主动检索的基本原理,给出了相应的算法描述,并通过实验证明了该模型具有可行性.     

A content search system considering the activity and context of a mobile user

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user’s activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user’s degree of concentration on his or her mobile device’s display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user’s activity and context, with the goal of reducing the user’s operation load for content search. The proposed system switches between two kinds of content search systems according to the user’s activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.     

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Web集群的海量影像显示技术研究

针对目前Web显示技术只允许单个用户通过远程视频会议和桌面镜像与单个异地用户协同工作,不能满足多用户海量信息共享显示的要求,不支持海量影像超高分辨显示需要等问题.本文根据多用户海量信息共享显示的特点以及显示的难点,研究了基于Web集群海量影像多异地用户在拼接显示屏上超高分辨显示等技术.实验结果表明,提出的Web集群并行拼接海量影像显示技术是有效的,能够同时为多个异地用户的海量影像在拼接显示屏上超高分辨显示提供可行的解决方案.     

NEWER: A system for NEuro-fuzzy WEb Recommendation

In the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by providing them with information and/or services that they need without explicitly asking for them. In this paper we propose NEWER, a usage-based Web recommendation system that exploits the potential of Computational Intelligence techniques to dynamically suggest interesting pages to users according to their preferences. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to discover a recommendation model as a set of fuzzy rules expressing the associations between user categories and relevances of pages. The discovered model is used by a online recommendation module to determine the list of links judged relevant for users. The results obtained on both synthetic and real-world data show that NEWER is effective for recommendation, leading to a quality of the generated recommendations comparable and often significantly better than those of other approaches employed for the comparison.     

A query-oriented XML text summarization for mobile devices

Extensible Markup Language (XML) is a simple, flexible text format derived from SGML, which is originally designed to support large-scale electronic publishing. Nowadays XML plays a fundamental role in the exchange of a wide variety of data on the Web. As XML allows designers to create their own customized tags, enables the definition, transmission, validation, and interpretation of data between applications, devices and organizations, lots of works in soft computing employ XML to take control and responsibility for the information, such as fuzzy markup language, and accordingly there are lots of XML-based data or documents. However, most of mobile and interactive ubiquitous multimedia devices have restricted hardware such as CPU, memory, and display screen. So, it is essential to compress an XML document/element collection to a brief summary before it is delivered to the user according to his/her information need. Query-oriented XML text summarization aims to provide users a brief and readable substitution of the original retrieved documents/elements according to the user’s query, which can relieve users’ reading burden effectively. We propose a query-oriented XML summarization system QXMLSum, which extracts sentences and combines them as a summary based on three kinds of features: user’s queries, the content of XML documents/elements, and the structure of XML documents/elements. Experiments on the IEEE-CS datasets used in Initiative for the Evaluation of XML Retrieval show that the query-oriented XML summary generated by QXMLSum is competitive.     

Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System

A case study in adaptive information filtering systems for the Web is presented. The described system comprises two main modules, named HUMOS and WIFS. HUMOS is a user modeling system based on stereotypes. It builds and maintains long term models of individual Internet users, representing their information needs. The user model is structured as a frame containing informative words, enhanced with semantic networks. The proposed machine learning approach for the user modeling process is based on the use of an artificial neural network for stereotype assignments. WIFS is a content-based information filtering module, capable of selecting html/text documents on computer science collected from the Web according to the interests of the user. It has been created for the very purpose of the structure of the user model utilized by HUMOS. Currently, this system acts as an adaptive interface to the Web search engine ALTA VISTA^TM. An empirical evaluation of the system has been made in experimental settings. The experiments focused on the evaluation, by means of a non-parametric statistics approach, of the added value in terms of system performance given by the user modeling component; it also focused on the evaluation of the usability and user acceptance of the system. The results of the experiments are satisfactory and support the choice of a user model-based approach to information filtering on the Web.     

A novel mobile recommender system for indoor shopping

With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users’ preferences by analyzing users’ positions, without requiring users’ explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

Building Web Knowledge Flows based on Interactive Computing with Semantics

Web personalized services alleviate the burden of information overload by providing right information which meets individual user’s needs. How to obtain and represent knowledge needed by users is a key issue. This paper presents Web Knowledge Flow (WKF) to represent the specific knowledge on Web pages and a model of Interactive Computing with Semantics (ICS) to provide a feasible means of generating WKF. Objective WKF (OWKF) and Real-time WKF (RWKF) are firstly proposed to satisfy staged and real-time user interests. Secondly, the generation algorithm of WKF is proposed based on Semantics Link Network. Thirdly, “interactive point” is introduced to detect the moment of user interests change to ensures the dynamics of WKF. Experimental results demonstrate that ICS can effectively capture the change of user interests and the generated WKF can satisfy user requirements accurately.     

基于多变量Probit回归的用户兴趣挖掘方法

用户兴趣挖掘一直是很多领域的基础问题,例如推荐系统、个性化检索和在线广告。一个用户在Internet或现实生活中的历史行为虽然能反映用户的兴趣,但是如果用户第一次使用网络,因为缺少历史行为信息,系统很难获得用户的兴趣。为解决无法获取新用户兴趣的问题,本文提出一种基于多变量Probit回归的用户兴趣挖掘方法。采用马尔科夫链蒙特卡洛(MCMC)方法估计模型的后验分布。通过合成数据与豆瓣明星对电影的兴趣验证模型的性能,结果表明所提出的方法能够有效地预测冷启动用户的兴趣。