Exploring Generative AI Recommender Systems in E-Commerce: Model, Evaluation Metric, and Comparative Review
Article Sidebar
Main Article Content
Abstract
Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The purpose of this paper is to provide overall insight into how GAI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other emerging methods, is affecting the building and running of modern e-commerce RS. This paper classifies generative models into groups based on the type of models used, data modality, and specific domain of application. Their involvement in tasks such as personalized product ranking, content generation, and cold-start problem avoidance is discussed comprehensively as well. In addition, we also analyse innovation in design trends, practical challenges, such as explainability, real-time adaptability, computational scalability, and possible trade-offs, as well as pathways ahead through the lens of current literature and empirical systems. By contrasting GAI-RS with traditional RS, we highlight their advantages in handling several problems, such as data sparsity, generating diverse recommendations, and enabling dynamic user interaction. This paper should serve to broaden awareness among scholars and practitioners about the ever-changing convergence of GAI and intelligent recommendation structures within e-commerce, emphasizing both their transformative potential and operational complexities in practice.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
S.K.R. Sharma, and S. Gaur, “The role of artificial intelligence in personalized e-commerce recommendations,” International Journal for Research Publication and Seminar, vol. 15, no. 1, pp. 64–71, Mar. 2024, doi: 10.36676/jrps.v15.i1.09.
Q. Zhang and Y. Xiong, “Harnessing AI potential in e-commerce: Improving user engagement and sales through deep learning-based product recommendations,” Current Psychology, vol. 43, no. 38, pp. 30379–30401, Oct. 2024, doi: 10.1007/s12144-024-06649-3.
H. Yuan, and A. A. Hernandez, “User cold start problem in recommendation systems: A systematic review,” IEEE Access, vol. 11, pp. 136958–136977, 2023, doi: 10.1109/ACCESS.2023.3338705.
M. Jangid, and R. Kumar, “Deep learning approaches to address cold start and long tail challenges in recommendation systems: A systematic review,” Multimedia Tools and Applications, vol. 84, no. 5, pp. 2293–2325, Oct. 2024, doi: 10.1007/s11042-024-20262-3.
X. Ma, M. Li, and X. Liu, “Advancements in recommender systems: A comprehensive analysis based on data, algorithms, and evaluation,” unpublished, Jul. 2024.
Q. Wang et al., “Towards next-generation LLM-based recommender systems: A survey and beyond,” unpublished, Oct. 2024.
M.O. Ayemowa, R. Ibrahim, and M.M. Khan, “Analysis of recommender system using generative artificial intelligence: A systematic literature review,” IEEE Access, vol. 12, pp. 87742–87766, 2024, doi: 10.1109/ACCESS.2024.3416962.
D.P. Gohel and P. A. Vanjara, “Analyzing user-based and item-based recommender systems: A comparative examination,” Educational Administration: Theory and Practice, vol. 30, no. 6(S), Jun. 2024, doi: 10.53555/kuey.v30i6(S).5331.
T. Anwar, V. Uma, Md. I. Hussain, and M. Pantula, “Collaborative filtering and KNN based recommendation to overcome cold start and sparsity issues: A comparative analysis,” Multimedia Tools and Applications, vol. 81, no. 25, pp. 35693–35711, Oct. 2022, doi: 10.1007/s11042-021-11883-z.
S. Natarajan, S. Vairavasundaram, S. Natarajan, and A.H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data,” Expert Systems with Applications, vol. 149, pp. 113248, Jul. 2020, doi: 10.1016/j.eswa.2020.113248.
M. Liao and S.S. Sundar, “When e-commerce personalization systems show and tell: Investigating the relative persuasive appeal of content-based versus collaborative filtering,” Journal of Advertising, vol. 51, no. 2, pp. 256–267, Mar. 2022, doi: 10.1080/00913367.2021.1887013.
D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” Journal of Big Data, vol. 9, no. 1, pp. 59, Dec. 2022, doi: 10.1186/s40537-022-00592-5.
Y. Xu, E. Wang, Y. Yang, and H. Xiong, “GS²-RS: A generative approach for alleviating cold start and filter bubbles in recommender systems,” IEEE Transactions on Knowledge and Data Engineering, pp. 1–14, 2023, doi: 10.1109/TKDE.2023.3290140.
H. Lu et al., “DeepSeek-VL: Towards real-world vision-language understanding,” unpublished, Mar. 2024.
M.N.-U.-R. Chowdhury, A. Haque, and I. Ahmed, “DeepSeek vs. ChatGPT: A comparative analysis of performance, efficiency, and ethical ai considerations,” TechRxiv Preprint, Feb. 11, 2025, doi: 10.36227/techrxiv.173929663.35290537/v1.
A. Rasool, M.I. Shahzad, H. Aslam, V. Chan, and M.A. Arshad, “Emotion-aware embedding fusion in Large Language Models (Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4) for intelligent response generation,” AI, vol. 6, no. 3, pp. 56, Mar. 2025, doi: 10.3390/ai6030056.
R. Gupta, S. Tiwari, and P. Chaudhary, “Computational foundation of generative AI models,” in Generative AI: Concepts and Applications, Springer, 2025, pp. 23–44, doi: 10.1007/978-3-031-82062-5_2.
Y. Deldjoo et al., “Recommendation with generative models,” unpublished, Sep. 2024.
D. Liang, R.G. Krishnan, M.D. Hoffman, and T. Jebara, “Variational Autoencoders for collaborative filtering,” in Proceedings of the 2018 World Wide Web Conference on World Wide Web (WWW ’18), New York, NY, USA: ACM Press, pp. 689–698, 2018, doi: 10.1145/3178876.3186150.
R. Nahta, G. S. Chauhan, Y.K. Meena, and D. Gopalani, “Deep learning with the generative models for recommender systems: A survey,” Computer Science Review, vol. 53, pp. 100646, Aug. 2024, doi: 10.1016/j.cosrev.2024.100646.
J. Liu, C. Shi, C. Yang, Z. Lu, and P.S. Yu, “A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources,” AI Open, vol. 3, pp. 40–57, 2022, doi: 10.1016/j.aiopen.2022.03.002.
K.I. Roumeliotis, N.D. Tselikas, and D.K. Nasiopoulos, “Precision-driven product recommendation software: unsupervised models, evaluated by GPT-4 LLM for enhanced recommender systems,” Software, vol. 3, no. 1, pp. 62–80, Feb. 2024, doi: 10.3390/software3010004.
A.Y. Tsai et al., “Leveraging LLM reasoning enhances personalized recommender systems,” unpublished, Jul. 2024.
J. Lin et al., “How can recommender systems benefit from Large Language Models: A Survey,” ACM Transactions on Information Systems, vol. 43, no. 2, pp. 1–47, Mar. 2025, doi: 10.1145/3678004.
Z. Gao et al., “Mitigating the filter bubble while maintaining relevance,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA: ACM, Jul. 2022, pp. 2524–2531, doi: 10.1145/3477495.3531890.
D. Antognini and B. Faltings, “Fast multi-step critiquing for VAE-based recommender systems,” in Proceedings of the Fifteenth ACM Conference on Recommender Systems, New York, NY, USA: ACM, pp. 209–219, Sep. 2021, doi: 10.1145/3460231.3474249.
I. Shenbin, A. Alekseev, E. Tutubalina, V. Malykh, and S.I. Nikolenko, “RecVAE: A new Variational Autoencoder for Top-N recommendations with implicit feedback,” ACM Conference Paper, Dec. 2019, doi: 10.1145/3336191.3371831.
W. Ma, X. Chen, W. Pan, and Z. Ming, “VAE++: Variational Autoencoder for heterogeneous one-class collaborative filtering,” in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, New York, NY, USA: ACM, pp. 666–674, Feb. 2022, doi: 10.1145/3488560.3498436.
W. Shafqat and Y.-C. Byun, “A hybrid GAN-based approach to solve imbalanced data problem in recommendation systems,” IEEE Access, vol. 10, pp. 11036–11047, 2022, doi: 10.1109/ACCESS.2022.3141776.
E. Dervishaj, and P. Cremonesi, “GAN-based matrix factorization for recommender systems,” in Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, New York, NY, USA: ACM, Apr. 2022, pp. 1373–1381, doi: 10.1145/3477314.3507099.
P. Chonwiharnphan, P. Thienprapasith, and E. Chuangsuwanich, “Generating realistic users using Generative Adversarial Network with recommendation-based embedding,” IEEE Access, vol. 8, pp. 41384–41393, 2020, doi: 10.1109/ACCESS.2020.2976491.
F. Zhou, Y. Mo, G. Trajcevski, K. Zhang, J. Wu, and T. Zhong, “Recommendation via collaborative autoregressive flows,” Neural Networks, vol. 126, pp. 52–64, Jun. 2020, doi: 10.1016/j.neunet.2020.03.010.
P. Zhang et al., “TransGNN: Harnessing the collaborative power of transformers and Graph Neural Networks for recommender systems,” in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA: ACM, pp. 1285–1295, Jul. 2024, doi: 10.1145/3626772.3657721.
Z.M. Ziegler and A.M. Rush, “Latent normalizing flows for discrete sequences,” unpublished, Jan. 2019.
F. Wang, W. Liu, C. Chen, M. Zhu, and X. Zheng, “HCFRec: Hash collaborative filtering via normalized flow with structural consensus for efficient recommendation,” unpublished, May 2022.
H. Liu, Y. Wei, X. Song, W. Guan, Y.-F. Li, and L. Nie, “MMGRec: Multimodal generative recommendation with transformer model,” unpublished, Apr. 2024.
C. Li, L. Xia, X. Ren, Y. Ye, Y. Xu, and C. Huang, “Graph transformer for recommendation,” unpublished, Jun. 2023, doi: 10.1145/3539618.3591723.
S. J. Kalaiarasi, and K. Nimala, “Enhancing e-commerce product recommendations using LLMs and transformer-based deep learning architectures,” in 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), IEEE, pp. 1–8, Dec. 2024, doi: 10.1109/ICSES63760.2024.10910646.
J. Li, W. Zhang, T. Wang, G. Xiong, A. Lu, and G. Medioni, “GPT4Rec: A generative framework for personalized recommendation and user interests interpretation,” unpublished, Apr. 2023.
V. Chamola, S. Sai, R. Sai, A. Hussain, and B. Sikdar, “Generative AI for consumer electronics: Enhancing user experience with cognitive and semantic computing,” IEEE Consumer Electronics Magazine, vol. 14, no. 2, pp. 10–19, Mar. 2025, doi: 10.1109/MCE.2024.3387049.
I. Karabila, N. Darraz, A. El-Ansari, N. Alami, and M. El Mallahi, “BERT-enhanced sentiment analysis for personalized e-commerce recommendations,” Multimedia Tools and Applications, vol. 83, pp. 56463–56488, Dec. 2023, doi: 10.1007/s11042-023-17689-5.
Z. Zhang et al., “Co-optimize content generation and consumption in a Large Scale video recommendation system,” in Proceedings of the 18th ACM Conference on Recommender Systems, New York, NY, USA: ACM, pp. 762–764, Oct. 2024, doi: 10.1145/3640457.3688033.
T. Li et al., “BeautyGAN: Instance-Level facial makeup transfer with deep Generative Adversarial Network,” in Proceedings of the 26th ACM International Conference on Multimedia, New York, NY, USA: ACM, pp. 645–653, Oct. 2018, doi: 10.1145/3240508.3240618.
K. N, R. S. K. V, P. R. Rasal, R.J. Jadhav, M. Saidireddy, and K.G. Kharade, “Artificial intelligence-based smart cosmetics suggestion system based on skin condition,” in 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), IEEE, pp. 797–801, Dec. 2022, doi: 10.1109/ICACRS55517.2022.10029120.
T. Islam, A. Miron, X. Liu, and Y. Li, “Deep learning in virtual try-on: A comprehensive survey,” IEEE Access, vol. 12, pp. 29475–29502, 2024, doi: 10.1109/ACCESS.2024.3368612.
Y. Archana, “Gen AI-driven electronics: Innovations, challenges and future prospects,” unpublished, 2023.
T. Badriyah, E. T. Wijayanto, I. Syarif, and P. Kristalina, “A hybrid recommendation system for e-commerce based on product description and user profile,” in 2017 Seventh International Conference on Innovative Computing Technology (INTECH), IEEE, pp. 95–100, Aug. 2017, doi: 10.1109/INTECH.2017.8102435.
K. Al Jadda, “Recommendation in home improvement industry, challenges and opportunities,” in Proceedings of the 13th ACM Conference on Recommender Systems, New York, NY, USA: ACM, pp. 528, Sep. 2019, doi: 10.1145/3298689.3346960.
Q. Zhang, J. Lu, and Y. Jin, “Artificial intelligence in recommender systems,” Complex & Intelligent Systems, vol. 7, no. 1, pp. 439–457, Feb. 2021, doi: 10.1007/s40747-020-00212-w.
M. Mendoza and N. Torres, “Evaluating Content Novelty in Recommender Systems,” Journal of Intelligent Information Systems, vol. 54, no. 2, pp. 297–316, Apr. 2020, doi: 10.1007/s10844-019-00548-x.
Y.-M. Tamm, R. Damdinov, and A. Vasilev, “Quality metrics in recommender systems: Do we calculate metrics consistently?,” in Proceedings of the Fifteenth ACM Conference on Recommender Systems, New York, NY, USA: ACM, pp. 708–713, Sep. 2021, doi: 10.1145/3460231.3478848.
G. Shani and A. Gunawardana, “Evaluating recommendation systems,” in Recommender Systems Handbook, Boston, MA: Springer US, pp. 257–297, 2011, doi: 10.1007/978-0-387-85820-3_8.
Z. C. Lipton, C. Elkan, and B. Narayanaswamy, “Thresholding classifiers to maximize F1 score,” unpublished, Feb. 2014.
J. Chen, A. Muscoloni, I. Abdelhamid, Y. Wu, and C. V. Cannistraci, “Generalizing the AUC-ROC for unbalanced data, early retrieval and link prediction evaluation,” Preprints, Jul. 22, 2024, doi: 10.20944/preprints202209.0277.v2.
C. C. Aggarwal, “Evaluating recommender systems,” in Recommender Systems, Cham: Springer International Publishing, pp. 225–254, 2016, doi: 10.1007/978-3-319-29659-3_7.
A. Drif, H.E. Zerrad, and H. Cherifi, “EnsVAE: Ensemble Variational Autoencoders for recommendations,” IEEE Access, vol. 8, pp. 188335–188351, 2020, doi: 10.1109/ACCESS.2020.3030693.
J. Wang, G. Liu, J. Wu, C. Jia, and Z. Zhang, “Self-supervised Variational Autoencoder for recommender systems,” in 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, pp. 831–835, Nov. 2021, doi: 10.1109/ICTAI52525.2021.00132.
J. Liu, Y. Xiao, K. Zhu, W. Zheng, and C.-H. Hsu, “Hybrid Variational Autoencoder for collaborative filtering,” in 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 251–256, May 2022, doi: 10.1109/CSCWD54268.2022.9776247.
Z. Cui, J. Ma, C. Zhou, J. Zhou, and H. Yang, “M6-Rec: Generative pretrained language models are open-ended recommender systems,” unpublished, May 2022.
J. Feng, Q. Wang, Z. Huang, and L. Yang, “ICCVAE: Item concept causal Variational Auto-encoder for Top-N recommendation,” in 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), IEEE, pp. 908–913, Apr. 2023, doi: 10.1109/ICSP58490.2023.10248832.
C.-H. Zhou and Y.-L. Chen, “VCGAN: Variational Collaborative Generative Adversarial Network for recommendation systems,” in ICC 2023 - IEEE International Conference on Communications, IEEE, pp. 6324–6330, May 2023, doi: 10.1109/ICC45041.2023.10278637.
J. Jia et al., “LEARN: Knowledge adaptation from Large Language Model to recommendation for practical industrial application,” unpublished, May 2024.
C.-W. Huang, L. Dinh, and A. Courville, “Augmented normalizing flows: Bridging the gap between generative flows and latent variable models,” unpublished, Feb. 2020.
M.O. Ayemowa, R. Ibrahim, and M. M. Khan, “Analysis of recommender system using generative artificial intelligence: A Systematic Literature Review,” IEEE Access, vol. 12, pp. 87742–87766, 2024, doi: 10.1109/ACCESS.2024.3416962.
W. Wang, Y. Zhang, and T.-S. Chua, “Recommendation in the era of generative artificial intelligence,” in Generative Artificial Intelligence: Applications and Implications, Cham: Springer, pp. 201–221, 2025, doi: 10.1007/978-3-031-73147-1_8.
N. Ohsaka and R. Togashi, “Curse of ‘low’ dimensionality in recommender systems,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA: ACM, pp. 537–547, Jul. 2023, doi: 10.1145/3539618.3591659.
S. Bengesi, H. El-Sayed, M. K. Sarker, Y. Houkpati, J. Irungu, and T. Oladunni, “Advancements in Generative AI: A comprehensive review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers,” IEEE Access, vol. 12, pp. 69812–69837, 2024, doi: 10.1109/ACCESS.2024.3397775.
I. Pesovski, R. Santos, R. Henriques, and V. Trajkovik, “Generative AI for customizable learning experiences,” Sustainability, vol. 16, no. 7, pp. 3034, Apr. 2024, doi: 10.3390/su16073034.
Z. Chen, W. Gan, J. Wu, K. Hu, and H. Lin, “Data scarcity in recommendation systems: A survey,” ACM Transactions on Recommender Systems, vol. 3, no. 3, pp. 1–31, Sep. 2025, doi: 10.1145/3639063.
D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” Journal of Big Data, vol. 9, no. 1, pp. 59, Dec. 2022, doi: 10.1186/s40537-022-00592-5.
H.H. Rashidi et al., “Statistics of Generative Artificial Intelligence and nongenerative predictive analytics machine learning in medicine,” Modern Pathology, vol. 38, no. 3, pp. 100663, Mar. 2025, doi: 10.1016/j.modpat.2024.100663.
H. Du et al., “The age of Generative AI and AI-generated everything,” IEEE Network, vol. 38, no. 6, pp. 501–512, Nov. 2024, doi: 10.1109/MNET.2024.3422241.
H. Cao et al., “A Survey on generative diffusion models,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 2814–2830, Jul. 2024, doi: 10.1109/TKDE.2024.3361474.
M. Ibrahim et al., “Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges,” Computers in Biology and Medicine, vol. 189, pp. 109834, May 2025, doi: 10.1016/j.compbiomed.2025.109834.
J. Chan, and Y. Li, “Enhancing team diversity with Generative AI: A novel project management framework,” in 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, pp. 1648–1652, Jul. 2024, doi: 10.1109/COMPSAC61105.2024.00259.
Q. Dang, G. Zhang, L. Wang, S. Yang, and T. Zhan, “A Generative Adversarial Networks model based evolutionary algorithm for multimodal multi-objective optimization,” IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1–10, 2024, doi: 10.1109/TETCI.2024.3397996.
S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Business & Information Systems Engineering, vol. 66, no. 1, pp. 111–126, Feb. 2024, doi: 10.1007/s12599-023-00834-7.
X. Pan, “Enhancing efficiency and innovation with Generative AI,” Journal of Artificial Intelligence and Autonomous Intelligence, vol. 1, no. 1, pp. 72–81, 2024, doi: 10.54364/JAIAI.2024.1105.
Y. Xi et al., “Efficient and deployable knowledge infusion for open-world recommendations via Large Language Models,” ACM Transactions on Recommender Systems, Mar. 2025, doi: 10.1145/3725894.
R. Alatrash, R. Priyadarshini, and H. Ezaldeen, “Collaborative filtering integrated fine-grained sentiment for hybrid recommender system,” The Journal of Supercomputing, vol. 80, no. 4, pp. 4760–4807, Mar. 2024, doi: 10.1007/s11227-023-05600-w.
Y. Park et al., “Inference optimization of foundation models on AI accelerators,” in Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, pp. 6605–6615, Aug. 2024, doi: 10.1145/3637528.3671465.