Enhancing Recommendation Systems with Autoencoder-SVD and Transformer-Based Summarization: A Sentiment-Aware Approach Using GPT-2 and VADER

Muhi  Saadi Rahdi; Farsad Zamani Boroujeni; Aladdin Abdulhassan; Mehdi Akbari Kopayei; Keyvan Mohebbi

doi:10.48161/qaj.v5n4a2141

Authors

Muhi Saadi Rahdi Department of Computer Engineering, Isf.C., Islamic Azad University, Isfahan 81595-158, Iran;
Farsad Zamani Boroujeni Department of Computer Engineering, SR.C., Islamic Azad University, Tehran 14515-775, Iran; Artificial Intelligence and Data Analysis Research Center, SR.C., Islamic Azad University, Tehran 14515-775, Iran;
Aladdin Abdulhassan Information Network, Razi University, Kermanshah 67144-14971, Iran;
Mehdi Akbari Kopayei Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 85141-43131, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 85141-43131, Iran.
Keyvan Mohebbi Department of Computer Engineering, Isf.C., Islamic Azad University, Isfahan 81595-158, Iran;

DOI:

https://doi.org/10.48161/qaj.v5n4a2141

Keywords:

large language model, recommender systems, gpt-2-based summarization, autoencoder-SVD, rule-based sentiment analysis.

Abstract

Recommender systems play a crucial role in enhancing user experience on social networks. However, traditional methods face challenges in processing user information, such as the inability to effectively analyze textual reviews and the weakness in learning nonlinear and complex relationships between users and items. Additionally, many of these approaches fail to optimally integrate explicit information (such as, ratings) and implicit information (such as, sentiment analysis), leading to reduced recommendation accuracy. This paper proposes a novel approach to improve recommendation accuracy by integrating Autoencoder-SVD with language model-based summarization. In this method, the GPT-2 language model is employed to extract rich summaries from user textual reviews, while VADER is utilized for rule-based sentiment analysis. Furthermore, Autoencoder-SVD enables dimensionality reduction of the user-item rating matrix, and a transformer network enhances the recommendation process. The evaluation results on the Amazon Fine Foods and Amazon Clothes datasets indicate that the proposed model achieves significant improvements over benchmark methods such as SVD and NMF, reducing MAE to 0.3 on both datasets and lowering RMSE to 0.66 and 0.63, respectively. These findings demonstrate that the effective integration of explicit and implicit information within the proposed framework can enhance recommendation accuracy compared to previous methods.

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References

Di, Y., Shi, H., Wang, X., Ma, R., & Liu, Y. (2025). Federated recommender system based on diffusion augmentation and guided denoising. ACM Transactions on Information Systems, 43(2), 1-36.

Liu, Q., Hu, J., Xiao, Y., Zhao, X., Gao, J., Wang, W., Li, Q., & Tang, J. (2024). Multimodal recommender systems: A survey. ACM Computing Surveys, 57(2), 1-17.

Mohammed, M. A., Lakhan, A., Zebari, D. A., Abdulkareem, K. H., Nedoma, J., Martinek, R., ... & Tiwari, P. (2023). Adaptive secure malware efficient machine learning algorithm for healthcare data. CAAI Transactions on Intelligence Technology.

Banerjee, A., Mahmudov, T., Adler, E., Aisyah, F. N., & Wörndl, W. (2025). Modeling sustainable city trips: integrating CO 2 e emissions, popularity, and seasonality into tourism recommender systems. Information Technology & Tourism, 1-38.

Aljunid, M. F., Manjaiah, D., Hooshmand, M. K., Ali, W. A., Shetty, A. M., & Alzoubah, S. Q. (2025). A collaborative filtering recommender systems: Survey. Neurocomputing, 617, 128718.

Li, Y., Liu, K., Satapathy, R., Wang, S., & Cambria, E. (2024). Recent developments in recommender systems: A survey. IEEE Computational Intelligence Magazine, 19(2), 78-95.

Kapoor, N. R., Kumar, A., Kumar, A., Zebari, D. A., Kumar, K., Mohammed, M. A., ... & Albahar, M. A. (2022). Event-specific transmission forecasting of SARS-CoV-2 in a mixed-mode ventilated office room using an ANN. International Journal of Environmental Research and Public Health, 19(24), 16862.

Mao, C., Huang, S., Sui, M., Yang, H., & Wang, X. (2024). Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model. Advances in Computer, Signals and Systems, 8(5), 109-118.

Liu, N., & Zhao, J. (2024). Recommendation system based on deep sentiment analysis and matrix factorization. IEEE Access, 11, 16994-17001.

Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., & He, Q. (2020). A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3549-3568.

Guo, F., Wang, Z., Wang, X., Lu, Q., & Ji, S. (2024). Dual-view multi-modal contrastive learning for graph-based recommender systems. Computers and Electrical Engineering, 116, 109213.

Sharma, K., Lee, Y.-C., Nambi, S., Salian, A., Shah, S., Kim, S.-W., & Kumar, S. (2024). A survey of graph neural networks for social recommender systems. ACM Computing Surveys, 56(10), 1-34.

Hasan, E., Rahman, M., Ding, C., Huang, J. X., & Raza, S. (2025). Based recommender systems: a survey of approaches, challenges and future perspectives. ACM Computing Surveys, 58(1), 1-41.

Darraz, N., Karabila, I., El-Ansari, A., Alami, N., & El Mallahi, M. (2025). Integrated sentiment analysis with BERT for enhanced hybrid recommendation systems. Expert Systems with Applications, 261, 125533.

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38.

Ambulgekar, H., Pathak, M. K., & Kokare, M. (2019). A Survey on Collaborative Filtering: Tasks, Approaches and Applications. Proceedings of International Ethical Hacking Conference 2018,

Rossiiev, O. D., Shapovalova, N. N., Rybalchenko, O. H., & Striuk, A. M. (2025). A comprehensive survey on reinforcement learning-based recommender systems: State-of-the-art, challenges, and future perspectives. CEUR Workshop Proceedings,

Xue, F., He, X., Wang, X., Xu, J., Liu, K., & Hong, R. (2019). Deep item-based collaborative filtering for top-n recommendation. ACM Transactions on Information Systems (TOIS), 37(3), 1-25.

Papadakis, H., Papagrigoriou, A., Panagiotakis, C., Kosmas, E., & Fragopoulou, P. (2022). Collaborative filtering recommender systems taxonomy. Knowledge and Information Systems, 64(1), 35-74.

Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., & Salehi, M. (2018). Evaluating Collaborative Filtering Recommender Algorithms: A Survey. IEEE Access, 6, 74003-74024.

Melville, P., & Sindhwani, V. (2017). Recommender systems. Encyclopedia of Machine Learning and Data Mining, 1056-1066.

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., & Dong, Z. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.

Forouzandeh, S., Rostami, M., & Berahmand, K. J. a. p. a. (2020). Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens.

Tahmasebi, H., Ravanmehr, R., & Mohamadrezaei, R. (2021). Social movie recommender system based on deep autoencoder network using Twitter data. Neural Computing and Applications, 33(5), 1607-1623.

Mu, Y., & Wu, Y. (2023). Multimodal Movie Recommendation System Using Deep Learning. Mathematics, 11(4), 895.

Jain, G., Mahara, T., & Sharma, S. (2023). Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique. Procedia Computer Science, 218, 1834-1844.

Sridhar, S., Dhanasekaran, D., & Latha, G. (2023). Content-Based Movie Recommendation System Using MBO with DBN. Intelligent Automation & Soft Computing, 35(3).

Latrech, J., Kodia, Z., & Ben Azzouna, N. (2024). CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning. The Journal of Supercomputing, 80(1), 1160-1182.

Siddik, M. B. S., & Wibowo, A. T. (2023). Collaborative Filtering Based Food Recommendation System Using Matrix Factorization. JURNAL MEDIA INFORMATIKA BUDIDARMA, 7(3), 1041-1049.

Rostami, M., Farrahi, V., Ahmadian, S., Mohammad Jafar Jalali, S., & Oussalah, M. (2023). A novel healthy and time-aware food recommender system using attributed community detection. Expert Systems with Applications, 221, 119719.

Hiriyannaiah, S., GM, S., & Srinivasa, K. (2023). DeepLSGR: Neural collaborative filtering for recommendation systems in smart community. Multimedia Tools and Applications, 82(6), 8709-8728.

Mohammadpour, T., Bidgoli, A. M., Enayatifar, R., & Haj Seyyed Javadi, H. (2023). Efficient recommendations in collaborative filtering recommender system: A multi-objective evolutionary approach based on NSGA-II algorithm. International Journal of Nonlinear Analysis and Applications, 14(1), 785-804.

Huang, L., Guan, C.-R., Huang, Z.-W., Gao, Y., Wang, C.-D., & Chen, C. P. (2024). Broad recommender system: An efficient nonlinear collaborative filtering approach. IEEE Transactions on Emerging Topics in Computational Intelligence.

Siet, S., Peng, S., Ilkhomjon, S., Kang, M., & Park, D.-S. (2024). Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans. Applied Sciences, 14(6), 2505.

Imantho, H., Seminar, K. B., Damayanthi, E., Suyatma, N. E., Priandana, K., Ligar, B. W., & Seminar, A. U. (2024). An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History. Jurnal Keteknikan Pertanian, 12(1), 140-152.

Rostami, M., Vardasbi, A., Aliannejadi, M., & Oussalah, M. (2024). Emotional Insights for Food Recommendations. In N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald, & I. Ounis, Advances in Information Retrieval Cham.

Gupta, S., Bindal, A. K., & Prasad, D. (2025). Multimodal graph-based recommendation system using hybrid filtering approach. International Journal of Computing and Digital Systems, 17(1), 1-15.

Deldjoo, Y., & Di Noia, T. (2025). Cfairllm: Consumer fairness evaluation in large-language model recommender system. ACM Transactions on Intelligent Systems and Technology.

Di, Y., Shi, H., Ma, R., Gao, H., Liu, Y., & Wang, W. (2025). Fedrl: A reinforcement learning federated recommender system for efficient communication using reinforcement selector and hypernet generator. ACM Transactions on Recommender Systems, 4(1), 1-31.

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and machines, 30(4), 681-694.

Lehman, E., & Johnson, A. (2023). Clinical-t5: Large language models built using mimic clinical text. PhysioNet, 101, 215-220.

Vincentio, A. D., & Hansun, S. (2025). A Fine-Tuned BART Pre-trained Language Model for the Indonesian Question-Answering Task. Engineering, Technology & Applied Science Research, 15(2), 21398-21403.

Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.

Zhang, Y., Liu, C., Liu, M., Liu, T., Lin, H., Huang, C.-B., & Ning, L. (2024). Attention is all you need: utilizing attention in AI-enabled drug discovery. Briefings in bioinformatics, 25(1), bbad467.

Asthana, P., Barnwal, M., Yadav, A., Aggrawal, M., & Goel, M. (2024). VADER: A Lightweight and Effective Approach for Sentiment Analysis. 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT),

Elahi, E., Anwar, S., Al-kfairy, M., Rodrigues, J. J., Ngueilbaye, A., Halim, Z., & Waqas, M. (2025). Graph attention-based neural collaborative filtering for item-specific recommendation system using knowledge graph. Expert Systems with Applications, 266, 126133.

Tang, P., Zhu, S., & Alatas, B. (2025). Improving news recommendation accuracy through multimodal variational autoencoder and adversarial training. IEEE Access, 13, 85269-85278.

Spineli, L. M. (2024). Local inconsistency detection using the Kullback–Leibler divergence measure. Systematic Reviews, 13(1), 261.

Tripathi, A., Jain, R., & Tahiliani, K. (2024). A recommender system based on variants of singular value decomposition. In Data Analytics for Intelligent Systems: Techniques and solutions (pp. 11-11-11-15). IOP Publishing Bristol, UK.

Zhao, X., & Sun, Y. (2022). Amazon fine food reviews with BERT model. Procedia Computer Science, 208, 401-406.

Dang, C. N., Moreno-García, M. N., & Prieta, F. D. l. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21(16), 5666.

Maji, S., Maity, S., Das, J., & Majumder, S. (2024). An improved recommendation system based on neural matrix factorization. In 2023 4th International Conference on Intelligent Technologies (CONIT) (pp. 1-7). IEEE.