Text-Based Product Matching with Incomplete and Inconsistent Items Descriptions

Łukasik, S; Michałowski, A; Kowalski, PA; Gandomi, AH

Text-Based Product Matching with Incomplete and Inconsistent Items Descriptions

Łukasik, S Michałowski, A Kowalski, PA Gandomi, AH

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Computational Science – ICCS 2021, 2021, 12743, pp. 92-103
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Łukasik, S
dc.contributor.author	Michałowski, A
dc.contributor.author	Kowalski, PA
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-08-24T06:29:28Z
dc.date.available	2021-08-24T06:29:28Z
dc.date.issued	2021-01-01
dc.identifier.citation	Computational Science – ICCS 2021, 2021, 12743, pp. 92-103
dc.identifier.isbn	9783030779634
dc.identifier.uri	http://hdl.handle.net/10453/150223
dc.description.abstract	In recent years Machine Learning and Artificial Intelligence are reshaping the landscape of e-commerce and retail. Using advanced analytics, behavioral modeling, and inference, representatives of these industries can leverage collected data and increase their market performance. To perform assortment optimization – one of the most fundamentals problems in retail – one has to identify products that are present in the competitors’ portfolios. It is not possible without effective product matching. The paper deals with finding identical products in the offer of different retailers. The task is performed using a text-mining approach, assuming that the data may contain incomplete information. Besides the description of the algorithm, the results for real-world data fetched from the offers of two consumer electronics retailers are being demonstrated.
dc.format.extent	45
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Computational Science – ICCS 2021
dc.relation.isbasedon	10.1007/978-3-030-77964-1_8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Text-Based Product Matching with Incomplete and Inconsistent Items Descriptions
dc.type	Chapter
utslib.citation.volume	12743
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-08-24T06:29:27Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	12743
dc.location	Switzerland

Abstract:

In recent years Machine Learning and Artificial Intelligence are reshaping the landscape of e-commerce and retail. Using advanced analytics, behavioral modeling, and inference, representatives of these industries can leverage collected data and increase their market performance. To perform assortment optimization – one of the most fundamentals problems in retail – one has to identify products that are present in the competitors’ portfolios. It is not possible without effective product matching. The paper deals with finding identical products in the offer of different retailers. The task is performed using a text-mining approach, assuming that the data may contain incomplete information. Besides the description of the algorithm, the results for real-world data fetched from the offers of two consumer electronics retailers are being demonstrated.

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http://hdl.handle.net/10453/150223