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

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. R...

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Bibliographic Details
Published inBig data and cognitive computing Vol. 5; no. 3; p. 35
Main Authors Jbene, Mourad, Tigani, Smail, Saadane, Rachid, Chehri, Abdellah
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
Subjects
Artificial neural networks
Context
Customers
deep learning
e-commerce
Electronic commerce
Experimentation
Indicators
information retrieval
Language
Learning to Rank
Machine learning
Natural language processing
Neural networks
Product reviews
Queries
Ranking
Search engines
Semantics
sentiment analysis
Shopping
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Summary: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.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc5030035