Item Matching Based on Collection and Processing Customer Perception of Images Olga Cherednichenko [0000-0002-9391-5220] , Maryna Vovk [0000-0003-4119-5441] and Oksana Ivashchenko [0000-0003-3636-3914] National Technical University “Kharkiv Polytechnic Institute”, 2, Kyrpychova str., 61002 Kharkiv, Ukraine olha.cherednichenko@gmail.com, marihavovk@gmail.com Oksana_ivashchenko@ukr.net Abstract. The number of sellers and goods being sold on the e-marketplaces is growing, so the volume of data stored and processed by e-commerce information systems is increasing drastically. That is why the development of performance solutions is quite relevant. The given paper provides the approach of item match- ing based on the human perception of item images. The main goal of the study is to build a model for assessing the similarity of items. This paper provides a de- scription of a software product for comparing product images collected on online trading platforms. The user evaluates the product visually. The developed soft- ware implements the crowdsourcing data collecting based on the comparator identification method. The use of this method involves an experiment in which the user is offered two images, by comparing which the determined binary reac- tion is obtained. The results show the perspective of the mobile client application as part of an item matching system that aims to optimize the search for products on the Internet. Keywords: Product Matching, Crowdsourcing, Mobile Application, Customer Perception, Comparator Identification 1 Introduction Digital transformation in all over our life is expeditiously growing nowadays. The amount of sellers and buyers via the Internet has increased for the latest years drasti- cally. The current situation caused by Covid-19 makes new challenges for the commu- nity. Ukrainians like a lot of people in the world need to live in the circumstances of quarantine. It forces individuals to use online applications more often for purchasing goods. For some categories it is the first trial, for others, it can be commonplace. But in both cases, a problem of the huge amount of commodity propositions rises. In order to simplify the process of choosing the product for the buyer the amount of offered goods should be reduced by grouping them on similar features. In order to solve such a prob- lem, online trading platforms provide different tools like filtering, recommendation, and so on. The issue of item matching has been already treated in our previous papers [1, 2]. In our experiments, we collected descriptions of commodities from different Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).