@Article{VasconcelosBaldSant:2020:ApSuCo,
author = "Vasconcelos, Leandro Guarino de and Baldochi, Laercio Augusto and
Santos, Rafael Duarte Coelho dos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Federal de Itajub{\'a} (UNIFEI)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "An approach to support the construction of adaptive Web
applications",
journal = "International Journal of Web Information Systems",
year = "2020",
volume = "16",
number = "2",
pages = "171--199",
keywords = "Web Mining, Adaptive Web applications, User behavior analysis.",
abstract = "Purpose This paper aims to presents Real-time Usage 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 adapt
its user interface in real-time to enhance the user experience.
RUM provides two types of services as follows: support for the
detection of struggling users; and user profiling based on the
detection of behavior patterns. Design/methodology/approach RUM
leverages the previous study 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 detection of struggling users, making it
possible to identify these users as soon as few logs from their
interaction are processed. RUM also exploits log 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, allowing the
application developer 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 complete tasks. 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 user profiling based on usage patterns,
no existing solutions provide support for detecting users from
specific profiles or struggling users, in real-time, while they
are browsing Web applications. RUM also provides a toolkit that
allows the approach to be easily deployed in any Web
application.",
doi = "10.1108/IJWIS-12-2018-0089",
url = "http://dx.doi.org/10.1108/IJWIS-12-2018-0089",
issn = "1744-0084",
label = "lattes: 8626122636195184 1 VasconcelosBALDSant:2020:ApSuCo",
language = "en",
targetfile = "vasconcelos_approach.pdf",
urlaccessdate = "27 abr. 2024"
}