Towards Reliable Customer Satisfaction Prediction: An AIML-Driven Multi-Modal Approach for E-Commerce Platforms

E-commerce platforms generate massive amounts of user interaction data in the form of reviews, ratings, and purchase history. Accurate prediction of customer satisfaction from this multi-modal data is critical for improving user experience, enhancing personalization, and driving business growth. However, existing solutions suffer from several challenges, including the cold-start problem for new users and items, data sparsity in user–item interaction matrices, and the inability to combine multiple data modalities effectively. This proposes a novel AI/ML-driven multi-modal framework that integrates three complementary components: BERT-based textual embeddings for capturing the semantic and sentiment information in customer reviews, LightGCN-based graph embeddings for modeling collaborative user–item relationships and mitigating sparsity issues, and metadata feature encoders for incorporating user demographics, product attributes, and contextual signals. The outputs from these components are fused in a joint feature space and passed through a neural prediction layer to estimate customer satisfaction scores. The expected outcome is a robust, scalable, and explainable prediction system that achieves higher accuracy, handles cold-start scenarios effectively, and can be deployed as a real-time decision-support tool for e-commerce platforms through a Streamlit-based interface.

BERT, LightGCN, Customer Satisfaction Predic tion, Natural Language Processing, Graph Neural Networks

1. J. Su and H. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, 2009. [Google Scholar] [Crossref]

2. Yehuda Koren, Robert Bell, and Chris Volinsky, “Matrix Factorization Techniques for Recommender Systems,” IEEE Computer, Vol. 42, No. 8, pp. 30-37, August 2009. [Google Scholar] [Crossref]

3. R. Chen, J. Fan, M. Wu, R. Cheng, and M. Chai, “GXNSRec: Multi-behavior sequential recommender based on graph cross networks,” Expert Systems with Applications, vol. 297, pt. B, 1 Feb. 2026 [Google Scholar] [Crossref]

4. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin,“Attention Is All You Need,” Advances in Neural Information Processing Systems (NeurIPS), 2017. [Google Scholar] [Crossref]

5. Z. Zhou, Z. Chen, G. Ma, Z. Lin, Y. Tan, S. Wang, and C. Yang, “Enhanced recommendation with hypergraph mixture of experts,” Expert Systems with Applications, vol. 297, pt. B, 1 Feb. 2026 [Google Scholar] [Crossref]

6. Rianne van den Berg, Thomas N. Kipf, and Max Welling,“Graph Convolutional Matrix Completion,” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2018 [Google Scholar] [Crossref]

7. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec, “Graph Convolutional Neural Networks for Web-Scale Recommender Systems (PinSage),” KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018. [Google Scholar] [Crossref]

8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL-HLT, 2019. [Google Scholar] [Crossref]

9. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov, “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv preprint arXiv:1907.11692, 2019. [Google Scholar] [Crossref]

10. Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang, “LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation,” Proceedings of SIGIR '20: The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020. [Google Scholar] [Crossref]

11. F. S. Alamri, A. Rehman, B. Alghoffaily, A. Ahmed, and K. Saleem, "Leveraging RAG with Transformer for Context-Based Personalized Recommendations," IEEE Access, vol. 13, pp. 97479-97487, 2025. [Google Scholar] [Crossref]

12. G. A. Amran, X. Li, and A. A. Al-Bakhrani, “MUSE-Rec: Explainable multi-behavioral social e-commerce recommendation with integrated link prediction,” Expert Systems with Applications, ScienceDirect, 2025. [Google Scholar] [Crossref]

13. Chaudhari, A., Hitham Seddig, A. A., Raut, R., and Sarlan, A., "FedRecSys: A Federated Learning-Based Recommender System for Concept Drift Adaptation," IEEE Access, vol. 13, pp. 1–14, 2025, doi: 10.1109/ACCESS.2025.3557045. [Google Scholar] [Crossref]

14. Industry Research & Case Studies, “Enhancing Customer Satisfaction using Machine Learning Models,” Multiple Industry Reports, 2021–2023. [Google Scholar] [Crossref]

15. Nusrat Jahan, Mohd. Usama, and Huda Jawed, “Enhancing E-Commerce Sentiment Analysis with Advanced BERT Techniques,” International Journal of Innovative Science and Research Technology (IJISRT), Vol. 8, Issue 6, June 2023. [Google Scholar] [Crossref]

16. Scott M. Lundberg and Su-In Lee,“A Unified Approach to Interpreting Model Predictions (SHAP),”Advances in Neural Information Processing Systems (NeurIPS), 2017. [Google Scholar] [Crossref]

17. Qingyu Xie, Chao Zhang, and Jiliang Tang,“Explainable Recommendation: A Survey and New Perspectives,”Foundations and Trends® in Information Retrieval, Vol. 14, No. 1, 2020. [Google Scholar] [Crossref]

18. Hamed Zamani and W. Bruce Croft,“Learning a Joint Search and Recommendation Model from User-Item Interactions,”Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM), 2020. [Google Scholar] [Crossref]

19. Mengting Wan and Julian McAuley, “Item Recommendation on Monotonic Behavior Chains,”Proceedings of the 12th ACM Conference on Recommender Systems (RecSys), 2018. [Google Scholar] [Crossref]

20. Yongfeng Zhang and Xu Chen,“Explainable Recommendation: A Survey and New Perspectives, (Jacob Devlin, 2019)”ACM Transactions on Information Systems (TOIS), 2020. [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition