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Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search

Jbene Mourad, Tigani Smail, Saadane Rachid et Chehri Abdellah. (2021). Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search. Big Data and Cognitive Computing, 5, (3), p. 35.

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URL officielle: http://doi.org/10.3390/bdcc5030035

Résumé

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:2504-2289
Volume:5
Numéro:3
Pages:p. 35
Version évaluée par les pairs:Oui
Date:13 Août 2021
Identifiant unique:10.3390/bdcc5030035
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:deep learning, sentiment analysis, information retrieval, learning to Rank, e-commerce, search engines, apprentissage profond, analyse des sentiments, recherche d'informations, moteurs de recherche
Déposé le:13 avr. 2022 15:27
Dernière modification:13 avr. 2022 15:27
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Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

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