Developing the Key Attributes for Product Matching Based onthe Item’s Image Tag Comparison

Ескіз недоступний
Дата
2020
Автори
Cherednichenko, O. Yu.
Yanholenko, O.
Kanishcheva, O.
Чередніченко, О. Ю.
DOI
Назва журналу
Номер ISSN
Назва тому
Видавець
CEUR Workshop Proceedings
Анотація
With the constant growth of the number of products on e-marketplaces, buyers feel hard to find and choose items that would satisfy all their needs and expectations. Search and filtering algorithms of recommender systems, although are striving to help users, still fail quite often due to incomplete and inaccurate description of items. The given work suggests to combine analysis of bothitem description and item image in order to construct groups of similar items. Since a person can define whether two items are similar or not looking at two images and a brief description, it is suggested to form a set of similar items based on users’ judgments and then to extract the core of keywords for the specific type of prod-ucts. Further, it is proposed to use the given core to evaluate the similarity of any new itemaddedto the definite group. The case study deals with the building of the core of keywords for sneakers. The developed key attributes allow matching the items with a high precision, thus, proving the effectiveness of the method of the core construction.
Опис
Ключові слова
E-commerce , item’s images , similarity items , image similarity , images matching , tag similarity , key attributes
Бібліографічний опис
Cherednichenko, O., Yanholenko, O., & Kanishcheva, O. (2020). Developing the Key Attributes for Product Matching Based onthe Item’s Image Tag Comparison. Modern Machine Learning Technologies and Data Science (MoMLeT+DS 2020) : proceedings of the 2nd International Workshop. CEUR Workshop Proceedings, 2631, 237–247.
Cherednichenko O., Yanholenko O., Kanishcheva O. Developing the Key Attributes for Product Matching Based onthe Item’s Image Tag Comparison. Modern Machine Learning Technologies and Data Science (MoMLeT+DS 2020) : proceedings of the 2nd International Workshop. CEUR Workshop Proceedings. 2020. Vol. 2631. P. 237–247.