Revolutionizing E-Commerce: A Deep Learning-Based Sentiment-Driven Recommendation System

The proliferation of the internet and the rising trend of online shopping have contributed to significant changes in consumer behavior. With a vast array of options available, consumers often face decision fatigue and information overload, creating challenges in finding the most suitable products. In response, product recommendation systems have emerged as essential tools of e-commerce, providing customized recommendations to individual preferences. This paper suggests a novel Deep Learning (DL) framework for product recommendation systems, leveraging Sentiment Analysis (SA) to enhance recommendation accuracy and relevance. The proposed framework combines SA with Collaborative Filtering (CF) techniques to provide insightful recommendations based on user sentiments and historical interactions. Specifically, a hybrid model of Bidirectional Encoder Representations from Transformers (BERT) and Gated Recurrent Unit (GRU) is employed for sentiment analysis, offering robust sentiment insights from user reviews. Additionally, CF methods, including user-based and item-based approaches, are utilized to find trends and analogies in user-item interactions, further refining the recommendation process. The efficiency of the suggested framework is demonstrated through comprehensive performance evaluations, including accuracy, precision, recall, and F1-score metrics. The outcomes of the experiment indicate that the integrated SA-CF model performs better than existing methods, achieving superior accuracy and precision in product recommendations. The proposed framework offers a powerful solution to improve the user’s experience, engagement, and satisfaction in e-commerce infrastructure by delivering personalized and sentiment-aware product recommendations.