Sentiment and Fuzzy Aware Product Recommendation System Using HOA and FT-DBN in E- Commerce
B.V. Manikandan, P Rama, S. Chakaravarthi · Indian Journal of Science and Technology · 2023
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