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

Designing an Adaptive Web Navigation Interface for Users with Variable Pointing Performance

Many online services and products require users to point and interact with user interface elements. For individuals who experience variable pointing ability due to physical impairments, environmental issues or age, using an input device (e.g., a computer mouse) to select elements on a website can be difficult. Adaptive user interfaces dynamically change their functionality in response to user behavior. They can support individuals with variable pointing abilities by 1) adapting dynamically to make element selection easier when a user is experiencing pointing difficulties, and 2) informing users about these pointing errors. While adaptive interfaces are increasingly prevalent on the Web, little is known about the preferences and expectations of users with variable pointing abilities and how to design systems that dynamically support them given these preferences. We conducted an investigation with 27 individuals who intermittently experience pointing problems to inform the design of an adaptive interface for web navigation. We used a functional high-fidelity prototype as a probe to gather information about user preferences and expectations. Our participants expected the system to recognize and integrate their preferences for how pointing tasks were carried out, preferred to receive information about system functionality and wanted to be in control of the interaction. We used findings from the study to inform the design of an adaptive Web navigation interface, PINATA that tracks user pointing performance over time and provides dynamic notifications and assistance tailored to their specifications. Our work contributes to a better understanding of users' preferences and expectations of the design of an adaptive pointing system

AHyCo: a Web-Based Adaptive Hypermedia Courseware System

Adaptive hypermedia courseware systems resolve the problem of users’ disorientation in hyperspace through the adaptive navigation and presentation support. We describe the AHyCo (Adaptive Hypermedia Courseware) - an adaptive Web-based educational system for creation and reuse of adaptive courseware with emphasis on adaptive navigation support and lessons sequencing. The proposed model consists of the domain model, the student model, and the adaptive model. The system is composed of two environments: the authoring environment and the learning environment

Moving towards Adaptive Search

Information retrieval has become very popular over the last decade with the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as intranets and digital libraries. Such collections are the focus of the recently started AutoAdapt project1. The project seeks to aid user search by providing well-structured domain knowledge to assist query modification and navigation. There are two challenges: acquiring the domain knowledge and adapting it automatically to the specific interest of the user community. At the workshop we will demonstrate an implemented prototype that serves as a starting point on the way to truly adaptive search

Engineering Concern-Sensitive Navigation Structures: Concepts, Tools and Examples

Improving navigability in Web applications is a serious challenge for developers as this quality feature is essential for applications success. In this paper we present the concept of concern-sensitive navigation, a useful conceptual tool to improve navigation by profiting from the nature of application’s concerns. Concern sensitive navigation allows enriching Web pages with information, services or links related with the context in which pages are accessed.We show how our ideas are applied during the development process (e.g. by applying wise design strategies for separation of concerns) and can also be used by final users while adapting an application (e.g. by modding). Some examples of Web 2.0 sites are used to illustrate this last possibility. We also compare our research with other similar approaches such as the construction of adaptive Web applications.Facultad de InformáticaLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA

Engineering Concern-Sensitive Navigation Structures: Concepts, Tools and Examples

Improving navigability in Web applications is a serious challenge for developers as this quality feature is essential for applications success. In this paper we present the concept of concern-sensitive navigation, a useful conceptual tool to improve navigation by profiting from the nature of application’s concerns. Concern sensitive navigation allows enriching Web pages with information, services or links related with the context in which pages are accessed.We show how our ideas are applied during the development process (e.g. by applying wise design strategies for separation of concerns) and can also be used by final users while adapting an application (e.g. by modding). Some examples of Web 2.0 sites are used to illustrate this last possibility. We also compare our research with other similar approaches such as the construction of adaptive Web applications.Facultad de InformáticaLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA

Methods for adaptivity in intelligent web-based learning systems

There are two main methods for implementing adaptivity in intelligent web-based learning systems: adaptive presentation (or content-level adaptation) and adaptive navigation support (or link-level adaptation). In the systems that use an adaptive presentation method, the content of an adaptive hypermedia page is generated or assembled from pieces according to the user’s background and knowledge state. In such the page, narrowed and detailed deep information (in forms of multimedia or text) is provided for advanced users, while broader and less deep additional explanation is provided for novices. Adaptive navigation support is a method of helping users to find their paths of learning in hypermedia systems by adapting the way of presenting links to goals, knowledge, and preferences of individual users. It consists of all methods of altering visible links to support hyperspace navigation. Some technologies were distinguished from the points of view according to the way they adapt presentation of links: direct guidance, link sorting, link hiding, link annotation, link generation, and map adaptation. Based on recent research and applications, this simple taxonomy is developed further

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

Adaptive web-based educational application for autistic students

Adaptive web-based applications have proven successful in reducing navigation and comprehension problems in hypermedia documents. In this paper, we describe a toolkit that is offered as an adaptive Web-based application to help autistic students incorporate to high education. The toolkit has been developed using a popular CMS in which we have integrated a client-side adaptation library. The toolkit described here was tried out during workshops with autistic students at Leeds Becketts University to gather (mostly qualitative) feedback on the adaptation and privacy aspects of the Autism&Uni platform. That feedback was later used to improve the toolkit

WebPUM : a web-based recommendation system to predict user future movements.

Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy