Using Markov Chains for link prediction in adaptive web sites

The large number of Web pages on many Web sites has raised navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site

Mining web sites using adaptive information extraction

Adaptive Information Extraction systems (IES) are currently used by some Semantic Web (SW) annotation tools as support to annotation (Handschuh et al., 2002; Vargas-Vera et al., 2002). They are generally based on fully supervised methodologies requiring fairly intense domain-specific annotation. Unfortunately, selecting representative examples may be difficult and annotations can be incorrect and require time. In this paper we present a methodology that drastically reduce (or even remove) the amount of manual annotation required when annotating consistent sets of pages. A very limited number of user-defined examples are used to bootstrap learning. Simple, high precision (and possibly high recall) IE patterns are induced using such examples, these patterns will then discover more examples which will in turn discover more patterns, etc.peer-reviewe

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

Active Rules for Runtime Adaptivity Management

The trend over the last years clearly shows that modern Web development is evolving from traditional, HTML-based Web sites to fullfledged, complex Web applications, also equipped with active and/or adaptive application features. While this evolution unavoidably implies higher development costs and times, such implications are contrasted by the dynamics of the modern Web, which demands for even faster application development and evolution cycles. In this paper we address the above problem by focusing on the case of adaptive Web applications. We illustrate an ECA rule-based approach, intended to facilitate the management and evolution of adaptive application features. For this purpose, we stress the importance of decoupling the active logic (i.e. the adaptivity rules) from the execution of the actual application by means of a decoupled rule engine that is able to capture events and to autonomously enact adaptivity actions

Web Accessibility at IU

Discussion will center around the recently-adopted IU Web Accessibility Administrative Practice, including what web designers can do to design accessible web sites from the beginning of the design process. A brief overview of the web accessibility evaluation services provided by the web accessibility team at the Adaptive Technology and Accessibility Center will also be discussed. Brief mention will be made of the ATAC's work on Sakai/Oncourse accessibility as well as emerging standards for the CIC

An Adaptive neural network for understanding website usage patterns

As the importance of the Internet rises, the need to create more adaptive and more usable web sites also grows. Most improvements to a website requires some knowledge of the site\u27s users and how they are interacting with the pages. However, web professionals today have relatively few good options for capturing this information. Certainly, there are software and services to help summarize the basic information from the web site logs. This could mean keeping track of the frequency of visits for the individual web pages that make up a site counting how many times the overall web site is visited from a specific web location, or other basic statistics

Enhancing User Privacy in Adaptive Web Sites with Client-Side User Profiles

Web personalization is an elegant and flexible process of making a web site responsive to the unique needs of each individual user. Data that reflects user prefe-rences and likings, comprising therefore a user profile, are gathered to an adaptive web site in a non transpa-rent manner. This situation however raises serious privacy concerns to the end user. When browsing a web site, users are not aware of several important pri-vacy parameters i.e., which behavior will be monitored and logged, how it will be processed, how long it will be kept, and with whom it will be shared in the long run. In this paper we propose an abstract architecture that enhances user privacy during interaction with adaptive web sites. This architecture enables users to create and update their personal privacy preferences for the adaptive web sites they visit by holding their (user) profiles in the client side instead of the server side. By doing so users will be able to self-confine the personalization experience the adaptive sites offer, thus enhancing privacy

A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.

Prediction of user future movements and intentions based on the users’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized services, predicting user near future intentions, adaptive Web sites and customer profiling is recognized. A variety of the recommender systems for online personalization through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users’ future intentions systems cannot still satisfy users in the particular huge web sites. In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. The approach is based on the using longest common subsequence algorithm to classify current user activities to predict user next movement. We have tested our proposed model on the CTI datasets. The results indicate that the approach can improve the quality of the system for the predictions

Interacting with Web Hierarchies

Web site interfaces are a particularly good fit for hierarchies in the broadest sense of that idea, i.e. a classification with multiple attributes, not necessarily a tree structure. Several adaptive interface designs are emerging that support flexible navigation orders, exposing and exploring dependencies, and procedural information-seeking tasks. This paper provides a context and vocabulary for thinking about hierarchical Web sites and their design. The paper identifies three features that interface to information hierarchies. These are flexible navigation orders, the ability to expose and explore dependencies, and support for procedural tasks. A few examples of these features are also provide