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Siamese Neural Networks for Detecting Complementary Products
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-7236-4637
Bol Com, Utrecht, Netherlands.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-2748-8929
2021 (English)In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, Association for Computational Linguistics , 2021, p. 65-70Conference paper, Published paper (Refereed)
Abstract [en]

Recommender systems play an important role in e-commerce websites as they improve the customer journey by helping the users find what they want at the right moment. In this paper, we focus on identifying a complementary relationship between the products of an e-commerce company. We propose a content-based recommender system for detecting complementary products, using Siamese Neural Networks (SNN). To this end, we implement and compare two different models: Siamese Convolutional Neural Network (CNN) and Siamese Long Short-Term Memory (LSTM). Moreover, we propose an extension of the SNN approach to handling millions of products in a matter of seconds, and we reduce the training time complexity by half. In the experiments, we show that Siamese LSTM can predict complementary products with an accuracy of ~85% using only the product titles.

Place, publisher, year, edition, pages
Association for Computational Linguistics , 2021. p. 65-70
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-295201ISI: 000861067300010Scopus ID: 2-s2.0-85107441806OAI: oai:DiVA.org:kth-295201DiVA, id: diva2:1555464
Conference
16th Conference of the European-Chapter-of-the-Association-for-Computational-Linguistics (EACL), APR 19-23, 2021, ELECTR NETWORK
Note

Part of proceedings: ISBN 978-1-954085-04-6

QC 20210608

Available from: 2021-05-18 Created: 2021-05-18 Last updated: 2023-09-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

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Scopushttps://www.aclweb.org/anthology/2021.eacl-srw.10

Authority records

Angelovska, MarinaSheikholeslami, SinaPayberah, Amir H.

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • en-US
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Output format
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