Discerning appropriate reviews based on hierarchical deep neural network for answering product-related queries

• Published in: Journal of intelligent & fuzzy systems Vol. 43; no. 4; pp. 5263 - 5277
• Main Authors: Geetha, M.P., Karthika Renuka, D.
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2022
• Subjects: Artificial neural networks; Electronic commerce; Neural networks; Queries; Questions
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-212485

In recent years, E-Commerce is globally increasing among online purchaser, in which customer post product related queries for finding the best product in online shopping. Manually answering the product related queries in real-time, cause online traffic and practically not possible. So, automatic answering system is helpful for answering product related queries. But, the product queries are always in product-explicit, so discovering related product queries and recovering its responds is distinctly be impractical. Accordingly, we propose Hierarchical Deep Neural Network (HiDeNN) model using MOQA framework to discern the appropriate reviews based on Mixtures of Opinions Question Answering (MOQA). The Hierarchical Deep Neural Network provides discerning the most relevant review for queries and it also provides the relevant answer for specific product category queries. The proposed method is executed on Python and it provides 9.594% and 7.574% higher accuracy value for Discerning Appropriate Reviews compared with the existing method like Relevant Reviews for Answering Product-related Queries (MOQA-BERTQA+FLTR+PT) and IQA: Interactive Query Construction on Semantic Question Answering Systems (IQC-SQA). The experimental result indicates that the proposed MOQA- HiDeNN method can efficiently and accurately get the optimal global solutions for recognizing the appropriate discerning of most relevant review for queries and it also provides the relevant answer for specific product category queries.