Deep Learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce

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dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorAamir, Fatima
dc.contributor.authorSherafgan, Raheimeen
dc.contributor.authorArbab, Tehreem
dc.contributor.authorJamil, Akhtar
dc.contributor.authorClose Bhatti, Fazeel Nadeem
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2025-04-18T08:49:13Z
dc.date.available2025-04-18T08:49:13Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractSearching is the process of information retrieval utilizing specific criteria or keywords. Integrating search function-alities on e-commerce platforms enables users to efficiently locate exactly what they are searching for through keyword matching. Beyond conventional keyword matching, semantic search involves aligning products with customer queries by capturing the essence of the queries, thereby retrieving semantically related products from the pertinent catalog. Semantic search enhances the e-commerce shopping experience by allowing platforms to tailor responses to user preferences through an in-depth understanding of search intents. Challenges such as morphological variations, spelling errors, and the interpretation of synonyms, antonyms, and hypernyms are addressed through deep learning models de-signed for semantic query-product matching. This study conducts a comparative analysis of various semantic search methodologies and assesses their efficacy, incorporating deep learning strate-gies for query auto-completion and spelling corrections. The evaluation employs sentence transformer models to determine the optimal approach for semantic searching, gauged by nDCG, MRR, and MAP metrics. LSTM, BART, and n-gram models are also examined for auto-completion capabilities. The research analyzes the Amazon Shopping Queries Dataset and the Upstart Commerce catalog datasets. © 2024 IEEE.
dc.identifier.citationAamir, F., Sherafgan, R., Arbab, T., Jamil, A., Bhatti, F. N., & Hameed, A. A. (2024, April). Deep Learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce. In 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-9). IEEE.
dc.identifier.doi10.1109/ICMI60790.2024.10586148
dc.identifier.isbn979-835037297-7
dc.identifier.scopus2-s2.0-85202805763
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6620
dc.identifier.wosWOS:001282083300137
dc.identifier.wosqualityN/A
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectE-commerce
dc.subjectMachine Learning
dc.subjectNatural Language Processing (NLP)
dc.subjectSemantic Product Searching
dc.titleDeep Learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce
dc.typeConference Object

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