基于Web日志挖掘的个性化网络广告模型

网络广告的飞速发展使得广告的效果越来越受重视。通过研究如何从Web日志中挖掘出用户对于网络广告的交互行为,设计出个性化网络广告系统。对提高网络广告平台的效益,增强网络广告效果具有重要的意义。     

基于分类方法的Web站点实时个性化推荐

提出一种新的基于分类方法的实时个性化推荐方法，该文首先根据用户访问事务文法生成序列访问事务集，用于得到每个用户访问的序列特性并且便于分类器进行分类，然后利用该事务集训练一个多类分类器，作者通过推荐引擎得到每个用户的当前访问序列和用户当前请求页面，然后把该序列送入分类器进行分类，以得到用户的下面一些可能访问的页面，这些推荐页面的地址被附加到用户当前请求的页面的底部由推荐引擎返回以进行推荐，在这种方法中，用户不需要注册信息，推荐不打扰用户，可以为用户提供实时个性化的服务，实验表明这种方法是成功的。     

Advertising value and credibility transfer: attitude towards web advertising and online information acquisition

China is undergoing a transition from offline to online advertising, but little is known about the causal relationship regarding credibility transfer from non-web advertising to web advertising. Meanwhile, web advertising usage behaviour is largely overlooked. The purpose of this study is to explore the formation of customers' attitude towards web advertising (ATT) which further impacts their online information acquisition behaviour, from the perspectives of advertising value and credibility transfer. This paper develops a research model drawing upon advertising value, credibility transfer, ATT and tie. Data collected from Chinese online shopping customers were used to test the model. The results indicate that perceived informativeness, perceived entertainment and credibility contribute to the formation of ATT, which further impacts web advertising usage for getting information. Meanwhile, tie positively moderates the credibility transfer from non-web advertising to web advertising. These findings and their implications for theory and practice are discussed.     

基于本体的Web使用知识发现模型及应用

本体在Web上的应用能够有效解决Web信息共享的语义问题。该文提出了基于Web本体和服务器日志文件的知识发现模型，主要讨论了用户访问行为的表示、语义用户分布的定义及发现算法。最后介绍了Web使用知识发现模型在Web个性化系统中的应用。     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

The effect of web advertising visual design on online purchase intention: An examination across gender

With web advertising growing to be a huge industry, it is important to understand the effectiveness of web advertisement. In this study we investigate the effects of web advertising visual design (WAVD) purchasing intention within the framework of an integrated model. Nine hypotheses were developed and tested on a dataset of 316 observations collected via a questionnaire survey. The results of structural equation modeling (SEM) indicate that while web advertising visual cues influence consumers' purchasing intention through advertising attitudes and brand attitudes, they do not have direct effects on purchasing intention. Further results on the moderating role of gender suggest that web advertising visual cues have direct effect on consumers' purchasing intention for male groups but not for female groups. This study contributes to the understanding the role of visual dimensions in forming online purchase intentions.     

Web挖掘在网络广告点击欺诈检测中的应用

为有效解决网络广告中存在的点击欺诈问题,提出了一种基于Web挖掘算法的解决方案,并设计了一套点击欺诈检测模型.该模型通过对点击流进行时序分析、离群点挖掘、非线性分析等操作,能有效检测或屏蔽各类点击欺诈,有效屏蔽无意识的无效点击,并且在不影响广告展示速度的基础上显著提高检测点击欺诈的效率.实验结果表明,该解决方案可以有效检测采用手动或者利用计算机程序的方法模仿正常用户进行点击欺诈的行为,表明了模型的可行性和方案的有效性.     

基于前后端日志记录的个性化推荐系统

为了有效地吸引和留住用户,提高网站服务的质量,在原有个性化实现技术基础上,提出了一种前后端日志相结合的方式存取用户浏览信息,对用户浏览站点的行为进行跟踪,为Web日志挖掘提供更精确有效的信息.结合前后端日志记录相结合的策略,提出了一个可伸缩的,独立于具体Web站点的页面推荐系统架构.实验分析结果表明,该方式能更准确全面的收集用户数据,同时个性化模块以一种非侵入的方式与系统集成,提高了系统的灵活性,方便系统重用.     

基于动态网站的语义数据挖掘模型研究

WEB使用挖掘正逐渐成为WEB个性化服务领域的研究重点,它通过对用户历史使用信息的分析,实现网站的个性化服务。然而,由于动态网页对象URL结构的特殊性,导致了WEB使用挖掘在动态网站应用上的局限性。论文在基于WEB使用挖掘分析的基础上,针对动态网站数据挖掘和个性化服务,提出了语义数据挖掘模型,并详细描述该模型的挖掘流程。     

Web mining and privacy concerns: Some important legal issues to be consider before applying any data and information extraction technique in web-based environments

Web mining is a concept that gathers all techniques, methods and algorithms used to extract information and knowledge from data originating on the web (web data). A part of this technique aims to analyze the behavior of users in order to continuously improve both the structure and content of visited web sites. Behind this quite altruistic belief – namely, to help the user feel comfortable when they visit a site through a personalization process – there underlie a series of processing methodologies which operate at least arguably from the point of view of the users’ privacy.Thus, an important question arises; to what extent may the desire to improve the services offered through a web site infringe upon the privacy of those who visit it? The use of powerful processing tools such as those provided by web mining may threaten users’ privacy.Current legal scholarship on privacy issues suggests a flexible approach that enables the determination, within each particular context, of those behaviors that can threaten individual privacy. However, it has been observed that TIC professionals, with the purpose of formulating practical rules on this matter, have a very narrow-minded concept of privacy, primarily centered on the dichotomy between personal identifiable information (PII) and anonymous data.The aim of this paper is to adopt an integrative approach based on the distinctive attributes of web mining in order to determine which techniques and uses are harmful.     

Web usage and content mining to extract knowledge for modelling the users of the Bidasoa Turismo website and to adapt it

The tourism industry has experienced a shift from offline to online travellers and this has made the use of intelligent systems in the tourism sector crucial. These information systems should provide tourism consumers and service providers with the most relevant information, more decision support, greater mobility and the most enjoyable travel experiences. As a consequence, Destination Marketing Organizations (DMOs) not only have to respond by adopting new technologies, but also by interpreting and using the knowledge created by the use of these techniques. This work presents the design of a general and non-invasive web mining system, built using the minimum information stored in a web server (the content of the website and the information from the log files stored in Common Log Format (CLF)) and its application to the Bidasoa Turismo (BTw) website. The proposed system combines web usage and content mining techniques with the three following main objectives: generating user navigation profiles to be used for link prediction; enriching the profiles with semantic information to diversify them, which provides the DMO with a tool to introduce links that will match the users taste; and moreover, obtaining global and language-dependent user interest profiles, which provides the DMO staff with important information for future web designs, and allows them to design future marketing campaigns for specific targets. The system performed successfully, obtaining profiles which fit in more than 60% of cases with the real user navigation sequences and in more than 90% of cases with the user interests. Moreover the automatically extracted semantic structure of the website and the interest profiles were validated by the BTw DMO staff, who found the knowledge provided to be very useful for the future.     

CATCH: A detecting algorithm for coalition attacks of hit inflation in internet advertising

As the Internet flourishes, online advertising becomes essential for marketing campaigns for business applications. To perform a marketing campaign, advertisers provide their advertisements to Internet publishers and commissions are paid to the publishers of the advertisements based on the clicks made for the posted advertisements or the purchases of the products of which advertisements posted. Since the payment given to a publisher is proportional to the amount of clicks received for the advertisements posted by the publisher, dishonest publishers are motivated to inflate the number of clicks on the advertisements hosted on their web sites. Since the click frauds are critical for online advertising to be reliable, the online advertisers make the efforts to prevent them effectively. However, the methods used for click frauds are also becoming more complex and sophisticated.     

Web Usage Mining as a Tool for Personalization: A Survey

This paper is a survey of recent work in the field of web usage mining for the benefitof research on the personalization of Web-based information services. The essence of personalization is the adaptability of information systems to the needs of their users. This issue is becoming increasingly important on the Web, as non-expert users are overwhelmed by the quantity of information available online, while commercial Web sites strive to add value to their services in order to create loyal relationships with their visitors-customers. This article views Web personalization through the prism of personalization policies adopted by Web sites and implementing a variety of functions. In this context, the area of Web usage mining is a valuable source of ideas and methods for the implementation of personalization functionality. We therefore present a survey of the most recent work in the field of Web usage mining, focusing on the problemsthat have been identified and the solutions that have been proposed.     

个性化Web推荐服务研究

本文主要论述了个性化Web推荐构成,提出了基于Web挖掘的个性化推荐服务研究中的用户聚类、Web页面聚类、n元预测模型及页面加权算法。利用这些算法得到的个性化信息可以准确把握用户兴趣模式并为用户提供“一对一”的具备自适应性的智能个性化服务。     

Can avatar and self-referencing really increase the effects of online 2-D and 3-D advertising?

Previous studies have sought insights into how websites can effectively draw sustained attention from internet users. Do different types of information presentations on webpages have different influences on users’ perceptions of the information? More precisely, can combinations of an ever greater number of advertising elements on individual websites increase consumers’ purchase intentions? The aim of this study is to explore changes in web advertising’s verbal and visual stimulation of surfers’ cognitive process, and to provide valuable information for the successful matching of advertising elements to one another. We examine optimal website design according to the personality-trait theory and resource-matching theory. Study 1 addresses the effects that combinations of various types of online advertising can have on web design factor, and to this end, we use a 2 (visual complexity: 3D advertising with an avatar, 2D advertising) × 2 (verbal complexity: with or without self-referencing that is an advertising practice to express product claims in words) factorial design. Study 2 treats personality traits (i.e., need-for-cognition and sensation seeking) as moderating variables to build the optimal portfolio regarding the “online-advertising effects” hypothesis. Our results suggest that subjects prefer medium-complex advertising comprising “3D advertising elements with an avatar” or “2D advertising elements with self-referencing”: high-sensation seekers and low-need-for-cognition viewers prefer the former, whereas low-sensation seekers and high-need-for-cognition viewers prefer the latter.     

How different information types affect viewer’s attention on internet advertising

The effect of internet advertising has been a controversial issue, especially on the topic of how to effectively draw more attention from internet users. According to traditional attention theory, we know people pay lesser attention on other objects if the main browsing contents occupy more of the viewer’s mental resources. Therefore, we know different information types of webpage should have different influences on users’ attention. On the other hand, an effect called ‘banner blindness’ makes viewers naturally overlook the advertising based on their previous experience. It is therefore becoming more and more difficult to increase viewer’s attention on advertisement simply by adding salient features on the advertisements. In light of this new challenge in Internet advertising, verifying the different influences of the information types on advertising attention is the main goal of this study.Great amounts of previous studies relevant to internet advertising focused on the advertisement itself, like the form, color, size and location. However, this study put focus on how the information types and the webpage structure influence the viewer’s attention on banner advertising. This research tested the effect on user attention of four common information types on Internet webpages: (1) text-based webpage; (2) text-picture mixed webpage; (3) picture-based webpage; and (4) video-based webpage. This study hopes to provide valuable information for matching advertising with viewing tasks that will stimulate the most user attention.     

基于Web挖掘的个性化技术研究

针对用户特性向用户提供个性化服务已经成为Web技术的研究热点。Web挖掘是实现Web个性化服务的关键技术之一。研究了Web挖掘技术,阐述了Web挖掘技术存在的不足,并对应用Web挖掘技术实现个性化服务的发展前景进行了分析。     

An architecture to support scalable online personalization on the Web

Online personalization is of great interest to e-companies. Virtually all personalization technologies are based on the idea of storing as much historical customer session data as possible, and then querying the data store as customers navigate through a web site. The holy grail of online personalization is an environment where fine-grained, detailed historical session data can be queried based on current online navigation patterns for use in formulating real-time responses. Unfortunately, as more consumers become e-shoppers, the user load and the amount of historical data continue to increase, causing scalability-related problems for almost all current personalization technologies. This paper chronicles the development of a real-time interaction management system through the integration of historical data and online visitation patterns of e-commerce site visitors. It describes the scientific underpinnings of the system as well as its architecture. Experimental evaluation of the system shows that the caching and storage techniques built into the system deliver performance that is orders of magnitude better than those derived from off-the-shelf database components. Received: 30 October 2000 / Accepted: 19 December 2000 Published online: 27 April 2001     

基于FLaAT的模糊用户偏爱浏览模式的增量挖掘

挖掘用户偏爱的浏览模式就是从Web日志中发现多数用户偏爱的浏览路径.网页上的浏览时间被转换成一个模糊语言变量来体现网页上浏览时间的特征,最后从建立的包含所有用户浏览信息的FLaAT(Frequent Link and Access Tree)中挖掘增量式带有模糊语言变量的用户偏爱浏览模式.     

关于提取Web用户浏览行为特征的研究

当前,Web日志挖掘技术已成为实现网站个性化服务的研究热点.运用Markov模型来预测用户的浏览模式,从而提高站点访问率、为站点重组提供有利信息是该领域广泛采用的方法之一.但传统方法建立的Markov模型,存在着数据冗余复杂、模型庞大繁琐等问题.针对这些问题,介绍了一种改进的Markov模型.其方法主要是在原有模型的基础之上,在数据清洗、用户会话识别过程中删除一些不予考虑的因素,大大简化了建立的Markov模型,提高了Web日志挖掘的效率.