User Behavioral Modeling for Web-based Systems
Creators: Sajjadi Ghaemmaghami, Saeedehsadat 
Publisher: name: University of Alberta Library
Year: 2022

<p>Today’s users are spending more time on web applications. Many users browse<br>
web applications and navigate through different web pages. They may have<br>
different interests, especially when it comes to large-scale applications. The more<br>
the developers of the applications know about their users’ needs and interests, the<br>
smarter choices they will make for their application’s development. Inferring a<br>
behavioral model from users’ navigation patterns in a web application helps<br>
application providers to understand their users’ interests. A navigational pattern is<br>
a record of where a user visits; the pattern is extracted from the start to the end of<br>
a user session. User navigation information is obtained by collecting the data in a<br>
log file.<br>
Some studies instrumented the application’s web pages to collect data and then<br>
model user navigational behaviors. To instrument a web page, the source code of<br>
the program is modified with additional commands. However, this can be difficult<br>
when the source code is inaccessible. Ideally, a user behavioral inference process<br>
should not be required to instrument the application’s web pages to generate a user<br>
behavioral model.<br>
Also, a model generation approach needs to support the evolution of web<br>
applications. A behavioral model should be generated incrementally during its<br>
evolution and should play a role in the application’s evolution (upgrading)<br>
procedure. This can help sustain web applications.<br>
Inferring a model by predicting and analyzing users’ navigational behaviors isiii<br>
necessary to understand users’ interests. Developers can identify interesting (from<br>
users’ perspective) or problematic pages of applications and therefore improve the<br>
application design. Analyzing the behavioral model helps to detect design<br>
anomalies such as dead-ends; pages in which users are being prevented from<br>
leaving the page without closing it. Detecting dead-ends can significantly help in<br>
addressing design anomalies and providing solutions to retain users. Satisfied<br>
customers are more likely to stay with the company and contribute to its success.<br>
It is ideal to analyze web pages to model user behaviors. Web page analysis<br>
methods utilize web page segmentation which is the process of segmenting a web<br>
page into different blocks, where each block contains similar components in terms<br>
of structural, visual, or contextual similarity. Current segmentation methods use<br>
the Document Object Model (DOM) structure of a web page and vision-based<br>
techniques to segment a web page. However, current methods do not consider<br>
semantic analysis to categorize pages. Semantic analysis includes extracting text<br>
from segmented blocks, computing textual similarity, and regrouping blocks.<br>
In this research, we attempt to bridge several gaps in all the above-mentioned<br>
areas. Firstly, we provide an automated approach, with no instrumentation, to<br>
generate user behavioral models. We evaluate the utility of our approach by using<br>
it on a large-scale mobile and desktop application. Also, we evaluate the evolving<br>
properties of interaction patterns against the inferred behavioral models using an<br>
analysis engine.<br>
Next, we present a new combination model of web page segmentation, namelyiv<br>
Fusion-Block, by dividing the content of a web page into blocks by initially<br>
considering human perception (inspired by Gestalt laws of grouping) and<br>
subsequentially re-segmenting initial similar blocks using semantic text similarity.<br>
Hence, in the next part of our research, we improve the segmentation model,<br>
namely Integrated-Block, by merging the DOM structure, vision-based, and textbased similarity metrics of web pages. Finally, to verify the effectiveness of our<br>
approach, we applied it to the public datasets and compared it with the five existing<br>
state-of-the-art algorithms. We demonstrate the value and novelty of the presented<br>
solutions using extensive evaluations throughout the thesis.</p>