User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business

Abstract

The development of information technologies in IT business increases the interest in executing machine learning models directly on the client browser, reducing the load on the server and the number of levels of access to it. At the same time, some features have advantages and disadvantages, associated with a smaller amount of information transmitted over the network, limited power of client devices, and others. Among modern client-side tools with machine learning capabilities, Tensorflow.js is suitable, which can be used to analyse user behaviour in web applications for classification and clustering models based on their behavioural patterns, predict future user behaviour trends, detect unusual or suspicious user actions, recommendation models based on their previous behaviour. The article analyses the features of implementation and the limitations associated with the use, specifically regarding the behaviour of users in social networks. The model was formed based on data from news posts on social networks Instagram and Facebook, with the following parameters of user activity, such as the number of likes, comments, and shares according to the post's text. These aspects are a significant addition to the tools that can be applied within the economic, technical, and other means of IT business development. Considering this, it is advisable to study the formation and development of the innovation management system in e-business in the future.

Full text

Path of Science. 2024. Vol. 10. No 5 ISSN 2413-9009
Section “Economics” 1001
User Behavior Analysis Using Web-based Machine Learning Features:
New Solutions for IT Business
Roman Mysiuk 1, Oleksii Kononenko 2, Andriy Svystovych 2, Oleksii Ozhyhov 2,
Nazar Osadets 2, Yuriy Kuchmak 2, Andrii Pohrebniak 2, Yuriy Honsor 2
1 Ivan Franko National University of Lviv
1 Unіversytetska Street, Lvіv, Ukraine, 79000
2 Lviv University of Business and Law
99 Kulparkіvska Street, Lviv, 79021, Ukraine
DOІ: 10.22178/pos.104-5
JEL Classification:
D11, D83, M29, M49
Receіved 27.04.2024
Accepted 25.05.2024
Publіshed onlіne 31.05.2024
Corresponding Author:
Roman Mysiuk
mysyuk@ukr.net
© 2024 The Authors. Thіs
artіcle іs lіcensed under a
Creatіve Commons Attrіbutіon
4.0 Lіcense
Abstract. The development of information technologies in IT business increases
the interest in executing machine learning models directly on the client browser,
reducing the load on the server and the number of levels of access to it. At the same
time, some features have advantages and disadvantages, associated with a smaller
amount of information transmitted over the network, limited power of client devices,
and others. Among modern client-side tools with machine learning capabilities,
Tensorflow.js is suitable, which can be used to analyse user behaviour in web
applications for classification and clustering models based on their behavioural
patterns, predict future user behaviour trends, detect unusual or suspicious user
actions, recommendation models based on their previous behaviour. The article
analyses the features of implementation and the limitations associated with the
use, specifically regarding the behaviour of users in social networks. The model was
formed based on data from news posts on social networks Instagram and
Facebook, with the following parameters of user activity, such as the number of
likes, comments, and shares according to the post's text. These aspects are a
significant addition to the tools that can be applied within the economic, technical,
and other means of IT business development. Considering this, it is advisable to
study the formation and development of the innovation management system in e-
business in the future.
Keywords: business; IT business; machine learning; tensorflow; user behaviour
analysis; data analysis; social network; data processing; е-business development.
INTRODUCTION
In today's world, analysing user behaviour helps
to understand the needs and preferences of users,
develop a personal algorithm for selecting content
in social networks, improve developed products
and offer new services. In addition, the analysis
helps to ensure Internet security and prevent
fraud. Thus, a new term, User Behavior Analytics
(UBA), appeared worldwide.
User Behavior Analytics (UBA) is a process of
tracking, analysing, and interpreting user actions
and interactions within a digital environment,
such as a website or application. It involves
collecting data on user behaviour, including
actions such as clicks, page views, form
submissions, and transactions, and using
analytical techniques to derive insights into user
preferences, patterns, and trends in the same way
as it is described in [1, 2]. By understanding how
users navigate and engage with digital platforms,
organisations can optimise user experiences,
enhance product offerings, and achieve business
objectives more effectively. UBA encompasses
various methods and tools, including data
collection through web analytics platforms,
segmentation techniques to group users based on
common characteristics, and predictive analytics
to anticipate future user behaviour [3, 4, 5, 6].
Ultimately, UBA enables organisations to make
data-driven decisions, improve user satisfaction,
and drive business growth in the digital realm.
Tensorflow.js tool provides an API for developing
machine learning models in JavaScript that runs
on the web. This feature makes it available to
many web developers and can be used for various

Path of Science. 2024. Vol. 10. No 5 ISSN 2413-9009
Section “Economics” 1002
tasks. Today's most common example is training
and running machine learning models directly in
browsers or on the server using Node.js. Servers
usually have more resources to handle such tasks
since models are stored and executed directly on
the client side. When calculating on the client side,
special attention should be paid to critical
parameters, which can be considered as they are
in articles [7, 8, 9].
Therefore, this can result in less data that needs to
be transferred over the network. This can be a
significant advantage for large models as it
reduces loading time. Some aspects involve
running the models directly on the client device,
which can help keep the data private as it never
leaves the user's device. Disadvantages include
the limited power of client devices, which can lead
to limitations in the size and complexity of models
that can be used.
The article aims to improve theoretical and
practical aspects of the IT business, which relate
to user behaviour analysis using web-based
machine learning features.
RESULTS AND DISCUSSION
Methods and features of implementation of
web-based machine learning. Implementing
web-based machine learning involves several
methods and features to deploy machine learning
models on web platforms effectively. One
approach is to leverage frameworks like
Tensorflow.js or PyTorch.js to deploy models
directly in the browser, enabling client-side
inference. Additionally, using RESTful APIs or
GraphQL endpoints allows seamless integration
of machine learning functionalities into web
applications, facilitating communication between
frontend and backend systems. Feature-wise,
scalability, security, and latency optimisation are
crucial considerations. Implementing techniques
like model quantisation, caching, and
asynchronous processing can enhance
performance and user experience, ensuring
smooth operation even with large-scale
deployments. Furthermore, incorporating user
feedback mechanisms and continuous model
monitoring enables iterative improvement and
maintenance of deployed machine learning
systems.
А. Web-Based Methods of User Behavior Analytics.
Web-based methods of user behaviour analytics
encompass a range of techniques aimed at
understanding and optimising user interactions
with web applications. These methods leverage
various data sources, tools, and analytical
approaches to gather insights into user behaviour
patterns, preferences, and engagement metrics.
One fundamental aspect of web-based user
behaviour analytics is the collection of data
related to user interactions with the website or
application. This data can include page views,
clicks, scroll depth, form submissions, time spent
on a page, and navigation paths. Tools like Google
Analytics, Adobe Analytics, or custom event-
tracking solutions are commonly used to gather
this data.
Once data is collected, it needs to be processed
and analysed to derive actionable insights. This
involves data cleaning, aggregation, and
visualisation techniques to uncover patterns and
trends within the user behaviour data.
Visualisation tools like Tableau, Power BI, or
custom dashboards can help present the data in a
meaningful and digestible format. Segmentation
is a crucial aspect of user behaviour analytics,
where users are grouped into segments based on
common characteristics or behaviours.
Segmentation can be based on demographic
information, geographic location, device type,
referral source, or user actions. By segmenting
users, organisations can better understand the
needs and preferences of different user groups
and tailor their web experiences accordingly.
Behavioural analysis techniques, such as funnel
and cohort analyses, track and optimise user
journeys through the website or application.
Funnel analysis involves mapping out the steps
users take to complete a specific goal, such as
making a purchase or signing up for a newsletter
and identifying drop-off points where users
abandon the process. Cohort analysis involves
grouping users based on shared characteristics
(e.g., sign-up date) and tracking their behaviour
over time to identify trends and patterns. Machine
learning and predictive analytics can further
enhance user behaviour analytics by identifying
patterns and making predictions based on
historical data [10]. For example, machine
learning algorithms can predict user churn,
recommend personalised content or products,
detect anomalies or fraudulent activities, and
optimise conversion rates.
Real-time analytics enables organisations to
monitor user behaviour and respond promptly to
emerging trends or issues [11, 12]. Real-time
analytics platforms like Google Analytics Real-

Path of Science. 2024. Vol. 10. No 5 ISSN 2413-9009
Section “Economics” 1003
Time or custom-built solutions allow
organisations to track user interactions as they
happen and make data-driven decisions on the fly.
A/B testing, or split testing, is a common
technique used in user behaviour analytics to
compare the performance of different variations
of a web page or feature. By randomly assigning
users to other variations and measuring key
metrics such as conversion rate or click-through
rate, organisations can determine which version
performs better and iterate accordingly. Privacy
and data security are paramount concerns in web-
based user behaviour analytics, particularly with
the increasing focus on data protection
regulations such as the General Data Protection
Regulation and the California Consumer Privacy
Act. Organisations must ensure that they collect
and process user data compliant and ethically,
with appropriate safeguards in place to protect
user privacy [13–16]. In conclusion, web-based
methods of user behaviour analytics encompass a
diverse set of techniques and tools aimed at
understanding, analysing, and optimising user
interactions with web applications. By leveraging
data-driven insights, organisations can improve
user experiences, drive engagement, and achieve
their business objectives effectively.
In Figure 1, the example of code for training model
and testing with a count of likes, comments, and
shares in the same way as it is described in [17].
Figure 1 – An example of code for training model and
testing with a count of likes, comments and shares
Step 1. The data is loaded from the "data.json" file,
where the input contains data on the number of
likes, comments, and shares for each text, and the
output includes labels that indicate the text's
interest. This can be done using standard
programming language tools or a library to work
with JavaScript Object Notation (JSON) data.
Step 2. A neural network is created from several
layers, which are used to analyse the input data
and determine the interest of the text. In the case
of text interest classification, dense layers are
usually used. For example, "tf.sequential()"
creates a sequential model.
Step 3. The model is compiled using the Adam
optimiser and the binary cross-entropy loss
function since this is a binary classification
problem. The Adam optimiser is a popular choice
for many neural network training tasks because it
usually helps speed up the training process and
improve model convergence. It combines two
other optimisation methods: Stochastic Gradient
Descent (SGD) and Adaptive Learning Rate
(AdaGrad) method. For binary classification
problems where each example can belong to only
one of two classes (e.g. "positive" or "negative",
"true" or "false"), the binary cross-entropy loss
function is often used because it is well suited for
estimating the difference between predicted and
actual values in such problems.
Step 4. The model is trained on input and output
data. The "fit" method is used for this, which
adapts the model parameters to the training data.
Step 5. After training, the model can be used to
predict the interestingness of new text by
providing input to the expected method input.
The "predict" method returns probability vectors
indicating the probability of each input sample
belonging to the classes.
Each row in the input list represents the input
characters for each text, where the first element
corresponds to the number of likes, the second to
the number of comments, and the third to the
number of shares. Each item in the output list
indicates whether the text is intriguing (1) or not
interesting (0) [17].
The model example for training can be set
similarly to that in Figure 2.
В. Features of Implementation Tensorflow.js.
Tensorflow.js offers cross-platform compatibility
for developing machine learning applications on
web browsers and Node.js, enabling client-side
inference for faster response times. It facilitates
model deployment by converting trained models
from Tensorflow or Keras into web-compatible
formats. With support for transfer learning and
customisation, developers can efficiently fine-

Path of Science. 2024. Vol. 10. No 5 ISSN 2413-9009
Section “Economics” 1004
tune models and experiment with different
architectures. Tensorflow.js seamlessly integrates
with web technologies like HTML and WebGL,
optimising performance through hardware
acceleration. Its security features, including
model encryption, protect sensitive data, while
continuous updates and a vibrant community
contribute to its ongoing development and
improvement.
Figure 2 – Part of training model example in data.json
Some details regarding the specifics of using web-
based machine learning can be highlighted.
Tensorflow.js allows you to run machine learning
models directly in the user's browser. This can
provide fast and efficient user behaviour analytics
without sending data to the server. Tensorflow.js
can be easily integrated with web applications,
allowing developers to create user behaviour
analysis tools. Using WebGL and other
technologies, Tensorflow.js can handle large
amounts of data and complex models, making it
suitable for extensive web services.
At the same time, there are some limitations to
using this approach, which are related to the
limited client resources. Sometimes, the amount
of data may be so large that processing it on the
client may be inefficient or impossible due to
limited device resources. Since machine learning
models run on the client device, this can raise
questions about user data privacy, mainly if the
data is analysed directly on the device.
The prospects of such an approach are associated
with some valuable features for data analysis.
Tensorflow.js can be the basis for building new
tools for analysing user behaviour directly in the
browser, providing new opportunities for
analytics and personalisation of web applications.
Local data processing and analysis approaches
can increase data-intensive web applications'
efficiency and speed. Analysing user behaviour
directly on the user's device can help keep their
data private because the data never leaves the
device.
Сomparison of client-side and server-side
implementation Тensorflow. Сomparisons
between client-side and server-side
implementations of Tensorflow include
differences in the execution environment,
hardware, privacy, scalability, security, network
usage, availability and some aspects described in
[18–22] as it is in Table 1.
Table 1 – Comparing performance and validity metrics
for web-based and server-based machine learning
Type of implementation of Tensorflow with
different aspects
Aspect
Client-Side
Server-Side
Location of
Execution
Runs in the
client's
browser or
device
Runs on
remote
servers
Latency
Relies on the
client's
hardware,
typically less
powerful
Can leverage
powerful
hardware on
the server
Scalability
Limited by
the client's
hardware
and browser
capabilities
Highly
scalable, as
it can utilise
cloud
computing
resources
Network Usage
Requires
downloading
model and
inference
code to the
client,
increasing
initial load
time and
network
usage
Requires
sending data
to the server
for
processing,
potentially
increasing
network
usage during
inference.
Real-time
Inference
Limited by
the client's
hardware
capabilities
and network
speed
Can achieve
real-time
inference
depending
on server
performance
and network
conditions

Path of Science. 2024. Vol. 10. No 5 ISSN 2413-9009
Section “Economics” 1005
Type of implementation of Tensorflow with
different aspects
Privacy
Concerns
Data
remains on
the client's
device,
potentially
reducing
privacy
concerns
Data is
transmitted
to and
processed
on external
servers,
raising
privacy
concerns
Hardware
Requirements
Relies on the
client's
hardware,
typically less
powerful
Can leverage
powerful
hardware on
the server.
Many different indicators can be used to evaluate
efficiency and reliability. For example, execution
speed is determined by the time required to
perform client and server device operations. The
resource usage indicator estimates CPU, memory,
and other resource usage on the client and server
devices. Another indicator is scalability, which
allows the system to expand to handle larger
volumes of data or a higher number of requests.
The security score can be evaluated based on the
security measures applied to the Tensorflow
client and server implementation [23, 24, 25, 26].
Estimate the data transfer between the client and
server environments during model training or
inference.
CONCLUSIONS
Based on the information provided, it can be
concluded that there is a growing interest in
executing machine learning models directly on
client browsers due to the development of
information technologies in the IT business. This
approach offers benefits such as reducing the load
on servers and minimising the number of network
access levels.
Tensorflow.js emerges as a suitable tool among
modern client-side machine learning capabilities,
enabling the analysis of user behaviour in web
applications. However, there are both advantages
and disadvantages associated with client-side
execution. On the positive side, there's a reduction
in network traffic due to processing data locally,
and it offers the potential for real-time analysis
without the need for constant server requests.
Nonetheless, there are limitations, such as the
restricted processing power of client devices,
which can affect the complexity and efficiency of
models. In analysing user behaviour in social
networks, Tensorflow.js presents opportunities
for building classification and clustering models
based on behavioural patterns. It can aid in
predicting future trends in user behaviour,
detecting unusual actions, and providing
personalised recommendations based on past
behaviour.
The article highlights the features and limitations
of implementing machine learning models for
analysing user behaviour on social networks,
mainly focusing on data from Instagram and
Facebook posts. A model can be trained to gain
insights into user engagement and preferences
within these platforms by considering parameters
like the number of likes, comments, and shares.
These aspects are a significant addition to the
tools that can be applied within the economic,
technical, and other means of IT business
development. Considering this, it is advisable to
study the formation and development of the
innovation management system in e-business in
the future.
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