An approach to support the construction of adaptive Web applications

• Pages: 171-199
• DOI: https://doi.org/10.1108/IJWIS-12-2018-0089
• Date: 26 February 2020
• Published date: 26 February 2020
• Author: Leandro Guarino Vasconcelos,Laercio Augusto Baldochi,Rafael Duarte Coelho Santos
• Subject Matter: Information & knowledge management,Information & communications technology,Information systems,Library & information science,Information behaviour & retrieval,Metadata,Internet

An approach to support the

construction of adaptive Web

applications

Leandro Guarino Vasconcelos

Center for Weather Forecast and Climate Studies,

National Institute for Space Research, Sao Jose dos Campos, Brazil

Laercio Augusto Baldochi

Institute of Mathematics and Computation, Federal University of Itajuba,

Itajuba, Brazil, and

Rafael Duarte Coelho Santos

National Institute for Space Research, Sao Jose dos Campos, Brazil

Abstract

Purpose –This paper aims to presents Real-timeUsage Mining (RUM), an approach that exploits the rich

information providedby client logs to support the construction of adaptive Web applications. The main goal

of RUM is to provide useful information about the behavior of users that are currently browsing a Web

application. By consumingthis information, the application is able to adapt its user interfacein real-time to

enhance the user experience. RUM provides two types of services as follows: support for the detection of

strugglingusers; and user profiling based on the detection of behavior patterns.

Design/methodology/approach –RUM leverages the previousstudy on usability evaluation to provide

a service that evaluates the usability of tasks performed by users while they browse applications. This

evaluation is based on a metric that allows the detectionof struggling users, making it possible to identify

these users as soon as fewlogs from their interaction are processed. RUM also exploitslog mining techniques

to detect usage patterns, which are then associated with user profiles previously defined by the application

specialist. After associating usage patterns to user profiles, RUM is able to classify users as they browse

applications, allowingthe applicationdeveloper to tailor the user interface according to the users’needs and

preferences.

Findings –The proposed approach was exploited to improve user experience in real-world Web

applications. Experiments showed that RUM was effective to provide support for struggling users to

completetasks. Moreover, it was also effective to detect usage patterns and associate them with user profiles.

Originality/value –Although the literature reports studies that explore client logs to support both the

detection of struggling users and the userprofiling based on usage patterns, no existing solutions provide

support for detecting users from specific profilesor struggling users, in real-time, while they are browsing

Web applications. RUM also providesa toolkit that allows the approach to be easily deployed in any Web

application.

Keywords Web mining, Adaptive Web applications, User behavior analysis

Paper type Research paper

1. Introduction

The web is present in every aspect of our lives. Applications and services such as social

media tools, multimedia communication, news gathering and diffusion, e-commerce and

banking, interaction with the government and so on are pervasiveand cause the amount of

information on the web to grow each day.

Adaptive Web

applications

171

Received28 December 2018

Revised24 June 2019

Accepted25 June 2019

InternationalJournal of Web

InformationSystems

Vol.16 No. 2, 2020

pp. 171-199

© Emerald Publishing Limited

1744-0084

DOI 10.1108/IJWIS-12-2018-0089

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/1744-0084.htm

Modern Web users expect applicationsto be tailored and to present the right content in a

proper way at the right time (Velasquez and Palade, 2008). To achieve this, it is of prime

importance to have knowledge about the users. Therefore, the analysis of their browsing

behaviors is becomingmore and more relevant.

Velasquez and Palade (2008) stated,more than a decade ago that future Web applications

would need to adapt their content and structure do fit its users’needs. This still has to

become a common practice: although adapting content is more common nowadays, this

happens because companies commonlypresent tailored ads to the users to maximize profit.

Adaptation of the Web applications’user interfaces accordingly to the users’needs and

preferences, however,is still incipient.

To understand the needs and preferences of users in Web applicationsit is important to

analyze their logs. The most common type of log data is provided by Web servers. Server

logs store information regarding application resources accessed by users, such as pages,

images and other files. Thus, these logs retain only the major decisions of the user when

visiting a website, that is, the clicks on links that request Web pages. It is also possible to

collect logs at the user’s browser. This approach, called client logging, requires the

instrumentation of Web pages with code –usually JavaScript –which is responsible for

logging the interactions with the application’s user interface, such as mouse actions, page

scrolling, keyboard actions and touch events (when touch screen displays are used).

Therefore, client logs can be much more informative than server logs, as they preserve the

actions that precedea decision.

Logs present a large amountof information that must be adequately processed to extract

higher-level knowledge about the users’interactions and to allow the creation of tools to

customize the user experience. Pierrakos et al. (2003) proposed Web usage mining

techniques for this task.

This article presents Real-timeUsage Mining (RUM), an approach that exploits the rich

information provided by client logs to support the construction of adaptive Web

applications. The main goal of RUM is to provide useful information about the behavior of

users that are currently browsing a Web application. By consuming this information, the

application is able to adaptcontent and structure in real-time to enhance the user experience.

RUM provides two types of servicesas follows:

(1) support for the detection of struggling users; and

(2) user profiling based on the detection of behavior patterns.

These services are independentand provide different views about the behavior of users.

Offering support for struggling users is important to retain visitors. Issues associated

with the user, such as low Web literacy, motor or cognition impairments and the effects of

aging prevent many users from successfully using Web applications (Crabb and Hanson,

2014). To tackle this problem, approaches such as multi-layered interfaces (Leung et al.,

2010;Shneiderman,2003) have been proposed. By offering simplified interfaces designedfor

people with interactionissues, as well as full-fledged interfaces designed for advanced users,

this approach may fight the “one size fits all”solution commonly found on most Web

applications. The challenge to make this approach work lies in the ability to transparently

detect strugglingand non-struggling users as soon as they start usingthe application.

Our previous work on usability evaluation (Vasconcelos and Baldochi, 2012;Gonçalves

et al., 2016a) was used to define RUM’s features and capabilities: RUM provides a service

that evaluates the usability of tasks while the users are interacting with the applications.

This evaluation provides a metric that allows the detection of struggling users, making it

possible to identify themas soon as few logs from their interaction are processed.

IJWIS

16,2

172