research-article

A machine learning approach for product matching and categorization

Editor: Claudia d’Amato Authors: Petar Ristoski, Petar Petrovski, Peter Mika, Heiko PaulheimAuthors Info & Claims

Semantic Web, Volume 9, Issue 5

Pages 707 - 728

https://doi.org/10.3233/SW-180300

Published: 01 January 2018 Publication History

Abstract

Consumers today have the option to purchase products from thousands of e-shops. However, the completeness of the product specifications and the taxonomies used for organizing the products differ across different e-shops. To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. In this paper, we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach we use structured product data as supervision for training feature extraction models able to extract attribute-value pairs from textual product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, which boost the performance of the feature extraction model, thus leading to better product matching and categorization performances. Furthermore, we use a deep Convolutional Neural Network to produce image embeddings from product images, which further improve the results on both tasks.

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Cited By

Bilal AMirza HHussain IAhmad A(2024)Investigating Influence of Google-Play Application Titles on SuccessBig Data Research10.1016/j.bdr.2024.10044336:COnline publication date: 28-May-2024
https://dl.acm.org/doi/10.1016/j.bdr.2024.100443
Zhang DLiu YYuan ZFu YChen HXiong H(2023)Multi-Faceted Knowledge-Driven Pre-Training for Product Representation LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320092135:7(7239-7250)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3200921
Bandyapadhyay SFomin FInamdar TSimonov K(2023)Proportionally Fair Matching with Multiple GroupsGraph-Theoretic Concepts in Computer Science10.1007/978-3-031-43380-1_1(1-15)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-43380-1_1
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Index Terms

A machine learning approach for product matching and categorization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Semantic Web

Semantic Web Volume 9, Issue 5

2018

180 pages

ISSN:1570-0844

EISSN:2210-4968

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© 2018 – IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

Publication History

Published: 01 January 2018

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Cited By

Bilal AMirza HHussain IAhmad A(2024)Investigating Influence of Google-Play Application Titles on SuccessBig Data Research10.1016/j.bdr.2024.10044336:COnline publication date: 28-May-2024
https://dl.acm.org/doi/10.1016/j.bdr.2024.100443
Zhang DLiu YYuan ZFu YChen HXiong H(2023)Multi-Faceted Knowledge-Driven Pre-Training for Product Representation LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320092135:7(7239-7250)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3200921
Bandyapadhyay SFomin FInamdar TSimonov K(2023)Proportionally Fair Matching with Multiple GroupsGraph-Theoretic Concepts in Computer Science10.1007/978-3-031-43380-1_1(1-15)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-43380-1_1
Zhang ZSong X(2022)An Exploratory Study on Utilising the Web of Linked Data for Product Data MiningSN Computer Science10.1007/s42979-022-01415-34:1Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1007/s42979-022-01415-3
Le MHinneburg A(2022)An Application of Learned Multi-modal Product Similarity to E-CommerceSimilarity Search and Applications10.1007/978-3-031-17849-8_3(25-39)Online publication date: 5-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-17849-8_3
Huang LShao WWang FXie WWong K(2021)Metric Learning Based Vision Transformer for Product MatchingNeural Information Processing10.1007/978-3-030-92185-9_1(3-13)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-92185-9_1
Petrovski PBizer C(2020)Learning expressive linkage rules from sparse dataSemantic Web10.3233/SW-19035611:3(549-567)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-190356
Primpeli APeeters RBizer C(2019)The WDC Training Dataset and Gold Standard for Large-Scale Product MatchingCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316609(381-386)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316609
Zhang ZParamita M(2019)Product Classification Using Microdata AnnotationsThe Semantic Web – ISWC 201910.1007/978-3-030-30793-6_41(716-732)Online publication date: 26-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-30793-6_41

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