Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

B Manikandan; P Rama; S Chakaravarthi

doi:10.17485/IJST/v16i27.430

Article

Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i27.430

Year: 2023, Volume: 16, Issue: 27, Pages: 2058-2067

Original Article

Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce

B Manikandan¹,^2*, P Rama³, S Chakaravarthi⁴

¹Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, 600073, Tamilnadu, India
²Department of Computer Science and Engineering, MNR College of Engineering and Technology, Sangareddy, Hyderabad, 502285, Telangana, India
³Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, 600073, Tamilnadu, India
⁴Department of Artificial Intelligence and Data Science,Panimalar Engineering College, Chennai, 600123, Tamilnadu, India

*Corresponding Author
Email: [email protected]

Received Date:25 February 2023, Accepted Date:20 June 2023, Published Date:17 July 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To identify and select the customers’ liked products by introducing a new product recommendation system. Methods: This work proposes a new product recommendation system that incorporates a new feature optimization method called Sentiment weighted Horse herd Optimization Algorithm (SHOA) to identify the most suitable words that help perform effective prediction. This work’s prediction process is carried out by applying a newly proposed Deep Belief Network incorporating fuzzy temporal features. This work uses two different Amazon datasets. The first dataset contains 51, 00,000 review comments about various products, including books and movies. The second dataset is built with 82,00,000 review comments on Toys and Games. These data sets consider the product id and review rate important features and are used to compare with all other available works through experimental results. Findings:The experiments have been conducted using the Amazon dataset and proved better than other recommendation systems in terms of effectiveness and efficiency through Precision, Recall, Serendipity and nDCG value. Novelty: The introduction of a new DBN with Fuzzy Temporal rules and the newly developed SHOA is novel in this work to recommend suitable products to the customer.

Keywords: Feature Optimization; CNN; LSTM; Product Recommendation System; Fuzzy Logic; Fuzzy Rules

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Copyright

© 2023 Manikandan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)