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

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Roshy Thomas, J. R. Jeba

10.14445/23488549/IJECE-V11I8P112

How to Cite?

Roshy Thomas, J. R. Jeba, "Revolutionizing E-Commerce: A Deep Learning-Based Sentiment-Driven Recommendation System," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 8, pp. 111-122, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P112

Abstract:

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.

Keywords:

Collaborative filtering, E-commerce, Product recommendation, Sentiment analysis, User preferences.

References:

[1] Jatin Sharma et al., “Product Recommendation System a Comprehensive Review,” IOP Conference Series: Materials Science and Engineering: 1st International Conference on Computational Research and Data Analytics, Rajpura, India, vol. 1022, no. 1, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Hyunwoo Hwangbo, Yang Sok Kim, and Kyung Jin Cha, “Recommendation System Development for Fashion Retail E-Commerce,” Electronic Commerce Research and Applications, vol. 28, pp. 94-101, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] I Wayan Rizky Wijaya, and Mudjahidin, “Development of Conceptual Model to Increase Customer Interest Using Recommendation System in E-Commerce,” Procedia Computer Science, vol. 197, pp. 727-733, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] K. Yogeswara Rao, G.S.N. Murthy, and S. Adinarayana, “Product Recommendation System from Users’ Reviews Using Sentiment Analysis,” International Journal of Computer Applications, vol. 169, no. 1, pp. 30-37, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yarasu Madhavi Latha, and B. Srinivasa Rao, “Amazon Product Recommendation System Based on a Modified Convolutional Neural Network,” ETRI Journal, vol. 46, no. 4, pp. 633-647, 2024.
[[CrossRef] [Google Scholar] [Publisher Link]
[6] Ramesh Vatambeti et al., “Twitter Sentiment Analysis on Online Food Services Based on Elephant Herd Optimization with Hybrid Deep Learning Technique,” Cluster Computing, vol. 27, no. 1, pp. 655-671, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Anisha P Rodrigues, and Niranjan N Chiplunkar, “Aspect Based Sentiment Analysis on Product Reviews,” 2018 Fourteenth International Conference on Information Processing, Bangalore, India, pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Rokibul Hasan, and Janatul Ferdous, “Dominance of AI and Machine Learning Techniques in Hybrid Movie Recommendation System Applying Text-to-number Conversion and Cosine Similarity Approaches,” Journal of Computer Science and Technology Studies, vol. 6, no. 1, pp. 94-102, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jesus Serrano-Guerrero et al., “A 2-Tuple Fuzzy Linguistic Model for Recommending Health Care Services Grounded on Aspect-Based Sentiment Analysis,” Expert Systems with Applications, vol. 238, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Gagandeep Kaur, and Amit Sharma, “A Deep Learning-Based Model Using Hybrid Feature Extraction Approach for Consumer Sentiment Analysis,” Journal of Big Data, vol. 10, no. 1, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] S. Bhaskaran, and Raja Marappan, “Design and Analysis of an Efficient Machine Learning Based Hybrid Recommendation System with Enhanced Density-Based Spatial Clustering for Digital E-Learning Applications,” Complex & Intelligent Systems, vol. 9, no. 4, pp. 3517- 3533, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] A. Suresh, and M.J. Carmel Mary Belinda, “Online Product Recommendation System Using Gated Recurrent unit with Broyden Fletcher Goldfarb Shanno Algorithm,” Evolutionary Intelligence, vol. 15, no. 3, pp. 1861-1874, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hengyun Li et al., “Restaurant Survival Prediction Using Customer-Generated Content: An Aspect-Based Sentiment Analysis of Online Reviews,” Tourism Management, vol. 96, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] N. Pavitha et al., “Movie Recommendation and Sentiment Analysis Using Machine Learning,” Global Transitions Proceedings, vol. 3, no. 1, pp. 279-284, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ning Liu, and Jianhua Zhao, “Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization,” IEEE Access, vol. 11, pp. 16994-17001, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Cach N. Dang, María N. Moreno-García, and Fernando De la Prieta, “An Approach to Integrating Sentiment Analysis into Recommender Systems,” Sensors, vol. 21, no. 16, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] A. Naresha, and P. Venkata Krishna, “Recommender System for Sentiment Analysis Using Machine Learning Models,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 10, pp. 583-588, 2021.
[Google Scholar] [Publisher Link]
[18] R.V. Karthik, and Sannasi Ganapathy, “A Fuzzy Recommendation System for Predicting the Customers Interests Using Sentiment Analysis and Ontology in E-Commerce,” Applied Soft Computing, vol. 108, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] I-Ching Hsu, and An-Hung Liao, “Sentiment-Based Chatbot Using Machine Learning for Recommendation System,” Researchsquare.
[CrossRef] [Google Scholar] [Publisher Link]
[20] BV Pranay Kumar, and Manchala Sadanandam, “A Fusion Architecture of BERT and RoBERTa for Enhanced Performance of Sentiment Analysis of Social Media Platforms,” International Journal of Computing and Digital Systems, vol. 15, no. 1, pp. 51-66, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Abien Fred Agarap, “A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data,” Proceedings of the 2018 10th International Conference on Machine Learning and Computing, Macau China, pp. 26-30, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Himani Srivastava et al., “A Novel Hierarchical BERT Architecture for Sarcasm Detection,” Proceedings of the Second Workshop on Figurative Language Processing, pp. 93-97, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] S. Zargar, “Introduction to Sequence Learning Models: RNN, LSTM, GRU,” Department of Mechanical and Aerospace Engineering, North Carolina State University, 2021.
[Google Scholar]
[24] Mingyang Pan et al., “Water Level Prediction Model Based on GRU and CNN,” IEEE Access, vol. 8, pp. 60090-60100, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Venkatasubramanian Sundaramahadevan, Sentiment Based Product Recommendation System, kaggle. [Online]. Available: https://www.kaggle.com/datasets/venkatasubramanian/sentiment-based-product-recommendation-system