=Paper=
{{Paper
|id=Vol-3179/Short_15.pdf
|storemode=property
|title=Use of Neural Networks for Pattern Recognition in E-Commerce
|pdfUrl=https://ceur-ws.org/Vol-3179/Short_15.pdf
|volume=Vol-3179
|authors=Ihor Ponomarenko,Igor Panasiuk,Volodymyr Pavlenko,Oleksandr Panasiuk,Oleg Kalmykov
|dblpUrl=https://dblp.org/rec/conf/iti2/PonomarenkoPPPK21
}}
==Use of Neural Networks for Pattern Recognition in E-Commerce==
https://ceur-ws.org/Vol-3179/Short_15.pdf
Use of Neural Networks for Pattern Recognition in E-Commerce
Ihor Ponomarenkoa, Igor Panasiuka, Volodymyr Pavlenkoa, Oleksandr Panasiukb and
Oleg Kalmykovc
a
Kyiv National University of Technologies and Design, Nemyrovycha-Danchenka Street, 2, Kyiv, 01011, Ukraine
b
Taras Shevchenko National University of Kyiv, Volodymyrska Street, 64/13, Kyiv, 01601, Ukraine
c
Hryhoriy Skovoroda University in Pereyaslav, Sukhomlynskу Street 30, Pereiaslav, 08401, Ukraine
Abstract.
The article is devoted to the substantiation of the expediency of using neural
networks for image processing in e-commerce. The differences between the
performance of classification and regression tasks due to the construction of neural
networks based on images are revealed. The main image processing algorithms that
have gained popularity in the scientific community and in the practice of private
companies are presented. The basic principles of neural network operation in the
process of image processing are presented.
Keywords 1
E-commerce, machine learning, modeling, neural network, optimization, image
recognition, digital channels.
1. Introduction
In modern conditions, there is an intensification of digitalization processes, which is associated with the
active development of innovative information technologies. Socio-economic transformations in the outlined
conditions lead to the reorientation of companies and consumers to the digital environment. The availability
of cloud storage and the development of appropriate algorithms have allowed the accumulation of large
amounts of data on an ongoing basis about any processes and phenomena on the Internet, as well as to
implement a variety of mathematical models. In modern conditions, the accumulated information should be
considered as a valuable resource for optimizing business processes.
An important area of the world economy in the field of services is e-commerce, which allows companies
to sell products of certain brands through the use of various digital channels. Significant competition in the
digital environment encourages companies to attract innovative approaches to find the target audience and
establish effective communications with potential customers on a long-term basis. Users focus on finding and
purchasing goods and services on the Internet based on their own preferences, based on different
characteristics: price, consumer quality, speed of delivery, prestige and more. To increase the level of
conversion, companies in the framework of e-commerce implement comprehensive marketing strategies,
which involves a comprehensive analysis of available information about key processes. Sources of information
for building appropriate mathematical models can be digital and textual information, video and audio content,
as well as graphics. In the field of e-commerce, when establishing communications with potential customers,
photos of certain products and a textual description of the main characteristics are actively used. The presented
approach is based on the desire of visitors to the web resource to visually evaluate the product of a particular
brand and decide on the appropriateness of purchasing the product or service.
Photos can serve as a valuable information resource that can be used to analyze various business processes,
but the process of transforming graphic objects into data requires a set of measures. At the present stage of
science development, machine learning methods have become widespread, which, thanks to the use of neural
networks of various architectures, allow for a comprehensive analysis of graphic objects. In the field of e-
commerce, the recognition of photographs through the use of neural networks is carried out to achieve various
Information Technology and Implementation (IT&I-2021), December 01–03, 2021, Kyiv, Ukraine
EMAIL: ponomarenko.iv@knutd.com.ua (A. 1); panasjuk.i@knutd.com.ua (A. 2); pavlenko.vm@knutd.edu.ua (A. 3); vohigi@gmail.com (A. 4);
olegkalmykov@ukr.net (A. 5)
ORCID: 0000-0003-3532-8332 (A. 1); 0000-0001-6671-4266 (A. 2); 0000-0003-2163-8508 (A. 3); 0000-0003-3338-4744 (A. 4); 0000-0002-1811-
5880 (A. 5)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
407
tasks, but the strategic goal is always to maximize profits in the long run. Accordingly, there are prerequisites
for further research to improve the methodological approaches to the recognition of graphic objects through
the use of neural networks to improve the efficiency of e-commerce.
2. Related works
The question of choosing machine learning methods for the analysis of existing graphic images is relevant
in modern conditions and is covered in the works of a large number of scientists. The analysis of professional
scientific publications and publications of specialists in the field of Data Science on various resources on the
Internet shows that in the process of pattern recognition various methods of machine learning are used. The
decision-making process regarding the choice of the appropriate method with a sufficient level of the results
accuracy is influenced by various factors: the sample size of graphic objects, quality characteristics of
photographs, professional skills and experience of analysts, time frame of the project, funding, computing
capabilities equipment, etc.
Peculiarities of using machine learning methods to build recommendation systems in e-commerce are
presented in [1]. Along with this, in [2] the peculiarities of using the system of recurrent neural networks for
large-scale categorization in e-commerce are revealed. The method proposed by the authors makes it possible
to avoid the problems of sparseness and scaling of data and to integrate the existing attributes into the overall
presentation.
The modern e-commerce system provides for close integration of companies' web resources with
appropriate payment systems, which simplifies the payment process for customers. The presence of a large
market for the sale of goods and services in the digital environment encourages fraudsters to develop various
schemes of illicit enrichment at the expense of all participants in e-commerce. The team of authors [3] devoted
research to the development of methods for using recurrent neural networks to identify fraudulent activities,
which involves the use of transaction data in e-commerce. For the security of customers' personal data,
convolutional neural networks are also used [4], which allow to implement user identification thanks to face
recognition technology.
The active development of e-commerce in the global environment has led to the development of various
approaches to the use of neural networks in the analysis of photo content large sets. Using multimodal neural
networks, a group of scientists has developed a system for forecasting the demand for goods through the use
of photographs and text markup, which makes it possible to increase the efficiency of filling specialized
catalogs with goods [5]. To improve the search for relevant content on the Internet, it is important to mark
information, including graphics. In addition, it is necessary to segment the photos used in e-commerce by
different characteristics (social, consumer, economic, demographic, psychological, etc.). Scientific work is
devoted to solving these problems in e-commerce [6].
Active development of computer technology and improvement of machine learning methods, first of all
development of more effective neural networks with the corresponding architecture, gives the chance to use
the received scientific approaches in the field of e-commerce. Graphic objects are a valuable source of
information about the phenomena and processes being studied, but each photo requires the use of a relatively
large amount of memory in the process of processing and constructing an appropriate mathematical model.
Based on these circumstances, there is a need for further study of the possibilities of using neural networks for
image processing in order to improve the efficiency of e-commerce.
3. Purpose
Modern companies in conditions of significant competition between market participants and the constant
introduction of innovative approaches are forced to actively seek innovation in the field of e-commerce. In the
process of achieving the goals, various digital marketing tools are used in inseparable combination with
machine learning approaches. Traditional statistical approaches do not allow optimizing key processes and
gaining competitive advantages. Also, statistical methods of data processing involve the use of only digital
information, which in some cases can be obtained on the basis of attributes. However, the modern development
of the digital environment involves the use of companies and users of content variety (data, text, photos, videos,
audio) [7, 8], which provided the use of scientifically sound approaches can be used as a valuable resource for
developing effective management decisions.
Based on the development of e-commerce in different areas in accordance with the needs of companies,
user needs and the specifics of the relevant markets, it is necessary to develop or improve methodological
approaches to neural networks that will transform existing visual content into effective management solutions.
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Accordingly, the main purpose of this study is the features of the use of neural networks for image recognition
in accordance with the needs of e-commerce. Figure 1 shows the main uses of neural networks in image
recognition to improve the efficiency of e-commerce.
There are two main areas of neural networks use in image recognition: classification and regression.
Consider each of the areas in more detail:
I. Classification.
1.1. Image recognition (classification) – this approach involves the definition of images and assignment to
a certain group in accordance with pre-formed characteristics, which are expressed in mathematical form due
to a certain system of constraints. The model allows companies to distinguish one object from another by
identifying the existing image by content [9].
NEURAL NETWORKS
Classification Regression
Image recognition Influence of factors
Image classification with Prognostication
localization
Associative rules
Object detection
Object (semantic) segmentation
Instance segmentation
Figure 1: The main directions of using neural networks in image recognition to increase the efficiency of
e-commerce [10, 11]
1.2. Image classification with localization – provides for the assignment of an object in the image to a
certain class and the selection of a single object using a bounding box, the area of which should be minimal
and limited by the size of the identified visual element. The accuracy of the model is characterized by the
reliability of the class identification for the object and the closest distances of the frame to the contours of the
object in width and height [12].
1.3. Object detection – is an advanced localization task, as it is necessary to select several objects in the
image, but the number of objects in the photo is not known in advance. The model involves detecting objects
by subtracting the corresponding coordinates and constraints using frames in accordance with a pre-designed
classification [13].
1.4. Object (semantic) segmentation – this approach involves dividing the image into separate squares with
the markup of each individual pixel in accordance with a pre-formed list of categories. For example, in e-
commerce it is possible to form an identification system that relates individual elements to parts of the human
or animal body, components of corporate identity, parts of products, parts of buildings and premises, and so
on [14].
1.5. Instance segmentation is the task of semantic segmentation of an image with object differentiation,
which involves identifying objects in a photograph and counting their number. The model allows to identify
on the studied image how many objects are in each formed group according to the used classification [15].
ІІ. Regression.
2.1. Influence of factors – this approach involves the transformation of photos into a numerical format and
use as factorial features that in some way affect the performance (volume of traffic, conversion rate, positive
feedback, increase the position of the company's web resource in search results, etc.) [16].
2.2. Forecasting is a task of building a multifactor forecasting model, which on the basis of graphically
converted graphical elements makes it possible to obtain predictive values of key performance indicators for
a certain period of time [17].
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2.3. Associative rules. This approach makes it possible to identify relationships between events or objects
that are implicit in unstructured data and involve the use of complex models to identify closeness. In
association-based e-commerce, it is possible to identify interconnected products or services on the basis of
graphic elements with appropriate text markup, and use the results to create referral systems [18].
4. Proposed technique
The process of using graphics as a valuable source of information to build effective neural networks
involves a set of steps, the first of which is the translation of photo content into digital form. The existing
image can be converted into a 2D function F (x, y), where x and y are the coordinates in space. The digital
image represents the amplitude F with finite values of x and y. It is also possible to convert the image into a
3D function with spatial coordinates x, y and z, the represented graphic object is called RGB (Red, Green,
Blue) [11].
It should be noted that the use of RGB color space has disadvantages because it is not possible to separate
color information from other data. The use of the RGB approach to image conversion has a negative effect on
the speed of neural network implementation, as it is necessary to use information about 3 channels in the
simulation process. An alternative approach involves the use of color space HSV (Hue, Saturation, Value),
which allows specialists to integrate color information into a single channel H (Hue) [19]. The practice of
using RGB and HSV shows the feasibility of using both approaches, because based on a set of factors for
different sets of images, the optimal result can be obtained by using the first or second color space. The main
image processing algorithms are Morphological Image Processing [20], Gaussian Image Processing [21],
Fourier Transform in image processing [22], Edge Detection in Image Processing [23], Wavelet Image
Processing [24], Image processing using Neural Networks [25].
Figure 2: Basic structure of a neural network [26]
Each approach has advantages and disadvantages in the process of image processing, but the greatest
prospects for improving the process of recognizing graphic objects, scientists associate with neural networks.
Neural networks are multilayer networks that are created from the basic units of data processing in the
system (neurons or nodes). The network works on the principle of functioning of the human brain: data are
obtained from the external environment, thanks to neurons there is modeling and training to recognize patterns
in information, at the last stage the forecast result is deduced. The basic neural network has three layers:
- Input layer;
- Hidden layer;
- The source layer [27, 28].
Figure 2 shows the basic structure of the neural network. Data is loaded into the neural network through
the input layers. The next stage is the process of calculations in hidden layers, the number of which is
determined empirically according to the specifics of the primary information and based on the level of
qualification of the analyst. In the process of improving the simulation results through the use of a neural
network, the number of layers is adjusted according to the value of the selected metrics.
The operation of the neural network in the process of image processing is based on the following principles:
410
1. The image is divided into pixels, and a single pixel acts as a neuron of the first layer.
2. Each channel is assigned a weight in the form of a probabilistic numerical value.
3. Weighted amounts are calculated as multiplying the weights by the corresponding input data, and the
result is used as input to the hidden layers of the neural network.
4. The selected activation function is used for the initial data, deciding whether to activate the neuron or
refuse further action.
5. The propagation of data to the next layers of the network occurs only due to activated neurons.
6. The output neuron on the layer is the value with the highest probability.
7. The error is calculated as the difference between the predicted and actual output. Due to backpropagation,
the results are transmitted back through the network.
8. A certain number of iterations of direct and reverse propagation of data with gradual adjustment of
weights are performed. When the optimal value is reached, the neural network stops the learning process.
Figure 3 shows the operations on the neural network, where a i is the i-th input, wi is the i-th weight, z is the
output, g is a certain activation function.
Figure 3: Operations for neural networks’ neuron [29]
In the process of building a neural network, it is necessary to determine the activation function. Figure 4
presents the main activation functions used in modern conditions. Among the above functions, the most used
is ReLU (linear equalizer with "leakage") [30, 31]. One of the main benefits of use ReLU is the reduced
likelihood that the gradient will disappear. The advantages of using ReLU include sparseness, as sparseness
occurs in cases of significant increase in units in the layer. In this case, as the number of units increases, the
resulting representation becomes more sparse.
1 10
Sigmoid Leaky ReLU
1
σ(x) = max(0.1x, x)
1 + 𝑒 −𝑥
-10 0 10
-1 10
1
tanh Maxout
-10 10
tanh(x) max(𝑤1𝑇 x + 𝑏1 , 𝑤2𝑇 x + 𝑏2 )
-1
10
10 ELU
ReLU 𝑥 𝑥≥0
൜ 10
𝑎(𝑒 𝑥 − 1) 𝑥 < 0 -10
max(0, x) -10 0 10 -2
Figure 4: Basic activation functions [32]
The importance of choosing a neural network architecture for image recognition, which will optimize the
results in the field of e-commerce, was noted above. The active development of machine learning algorithms
in combination with the growth of cloud services and the strengthening of computing capabilities have allowed
the use of neural networks with innovative architectures and to obtain higher quality results. Thanks to modern
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high-performance networks (Fig. 5) it is possible to achieve the identification of objects in the images in the
vast majority of cases and to improve the functioning of processes within the company's e-commerce [33].
Computer vision, which is realized through the use of appropriate neural networks, can significantly
improve the efficiency of companies in the digital environment by bringing the processing of visual objects to
a new level of quality. The above innovative neural networks for graphic image recognition are the next stage
in the development of data science. Due to the evolution of views and the development of progressive
algorithms, each neural network has its own life cycle, which ends after the emergence of more efficient
algorithms and the loss of relevance of this model.
COMPUTER VISION
EfficientNet EfficientDet SpineNet
CenterNet ThunderNet CSPNet
DenseNet SAUNet DetNASNet
SM-NAS AmoebaNet DPM
Graph Neural Growing Neural Spiking neural
Network Cellular Automata network
Figure 5: Neural networks for computer vision [34, 35, 36]
Modern scientists in the process of finding the best neural models to solve applied problems direct their
efforts to solve the following processes:
- automation of the process of finding the optimal parameters of the neural network according to the
specifics of the data and the implemented application tasks, taking into account the development of AutoML
approaches, «neural network generates neural network», Neural Architecture Search (NAS and NASNet) [37];
- choice of attention mechanism, attention cards;
- the use of advanced hourglass convolutional networks for object detection tasks in heterogeneous
visualizations, which are often used as backbone models in modular architectures;
- search for the optimal modularity system, as modern state-of-the-art architectures (SOTA) consist of a
large number of components [38].
5. Results
Interaction with modern customers in the digital environment is carried out in many cases through the use
of smartphones. A significant share of Generation Y users and an intensive increase in Generation Z purchasing
power is leading to extensive growth in mobile applications. In order to meet the growing demand, modern
companies in the process of implementing various e-commerce strategies bring to market innovative mobile
applications that help establish long-term communication with the target audience and maximize profits in
specific space-time conditions. Thanks to the use of machine learning methods, it is possible to optimize the
corresponding mobile application.
This study envisages optimizing the e-strategy for a clothing and footwear company. A large number of
Internet companies act as intermediaries between different brands and consumers. Due to the lack of physical
storage facilities for clothing and footwear, companies sign cooperation agreements with large warehouses and
order the appropriate products at the request of customers. It should be noted that today's customers have
significant opportunities to choose goods and are very quickly shifting from one supplier to another in the
digital environment. One of the characteristic features of Generation Y is the focus on visualized content and
the desire to search for the necessary things through the use of appropriate search engines.
In accordance with the scientific topic, the development and implementation of a mobile application is
envisaged, in which the searcher of the necessary clothes or shoes is integrated with the help of photography.
412
It is envisaged to photograph or download the required item in the module of the mobile application and its
identification using machine learning algorithms. Given the limited resources of companies, it is advisable to
use the available arrays of clothing and footwear photos to train the neural network. In this study, Fashion-
MNIST was used, which contains 60 thousand images to train the model and 10 thousand to test the quality of
the results. The use of a model Supervised learning is envisaged, as the primary data for the construction of
the neural network are marked. There are the following product categories:
0 – T-shirt/top;
1 – Trouser;
2 – Pullover;
3 – Dress;
4 – Coat;
5 – Sandal;
6 – Shirt;
7 – Sneaker;
8 – Bag;
9 – Ankle boot [39].
The process of selecting and improving the model involves the use of TensorFlow, which is an open library
for machine learning in the Python programming language. The flexibility applications of neural network
architectures can optimize solutions on an ongoing basis in order to obtain better solutions that will meet the
growing demands of today's target audience. The large amount of data makes it possible to significantly
improve the quality characteristics of the calculated model of machine learning. However, given the large
amount of training and test samples, each image was reduced to an array size of 28 × 28.
Figure 6 shows the increase in the accuracy of the model of identification of the item of clothing / footwear
for the following parameters:
- hidden layer of neurons: 110 neurons and relu activation function;
- source layer of neurons: 15 neurons and softmax activation function.
Figure 6: Increasing the accuracy of the selected machine learning model for image identification
At the next stage of checking the quality of the model, the test sample was loaded into the neural network
and the accuracy was determined. For the obtained model of clothing and footwear identification, the quality
on the test data was 0.8744. A small discrepancy in the values of the quality of the model for test data compared
to training indicates the acceptability of the use of the neural network in the corresponding mobile application.
The obtained model is used to assess the accuracy of identification of individual items of clothing and footwear
in the existing array of images. Figure 7 shows the accuracy of pullover identification the value of the indicator
was 99%. Figure 8 shows the accuracy of coat identification, the value of the indicator is 92%. The
implementation of the presented model of machine learning allows the application to use a recommendation
system, which based on user requests by uploading photos of clothes or shoes, provides the target audience
with a high probability of information about the necessary products. Thanks to the implementation of the neural
network in cloud storage and the use of powerful computing capabilities, image identification is implemented
very quickly.
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(a) Accuracy of pullover identification (b) Accuracy of coat identification
Figure 7: Accuracy of object identification [40]
6. Conclusions
In modern conditions, the recognition of graphic objects through the use of neural networks is carried out
through the use of specialized software. However, the best results are achieved through the use of programming
languages, primarily in the framework of Data Science uses the Python language. To facilitate the modeling
process within this programming language, specialized libraries are used for image transformation (OpenCV,
Scikit-Image, SciPy, NumPy, SimpleITK, etc.) and the construction of neural networks (TensorFlow and
Keras). The presented research shows the prospects of using neural networks both for image analysis in general
and for optimizing the functioning of e-commerce in particular. In the future, it is envisaged to improve
machine learning approaches and reorient to the mass use of artificial intelligence to increase the efficiency of
the e-commerce market. With the development of more sophisticated teaching methods, including neural
networks with innovative architectures, and the evolution of cloud services, more companies will be able to
perform relatively affordable data analysis.
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