Explorando la frontera: Aplicaciones generativas de la IA en el análisis del comportamiento de los consumidores en línea
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Publicado
11-09-2024
Takuma Kimura
https://orcid.org/0000-0001-7126-188X
Resumen
Este artículo presenta una revisión sistemática de la aplicación de la IA generativa en el Análisis del Comportamiento
del Consumidor Online (OCBA). Con la llegada del comercio electrónico y las redes sociales, el comportamiento de los
consumidores se produce cada vez más en línea, lo que genera enormes cantidades de datos. Este cambio requiere herra
mientas analíticas avanzadas, y la IA generativa emerge como una tecnología fundamental. La IA generativa, distinta de
la IA tradicional, puede generar de forma autónoma nuevos contenidos basados en patrones de datos aprendidos, ofre
ciendo enfoques innovadores a la OCBA. Basado en la metodología PRISMA (Preferred Reporting Items for Systematic
Reviews and Meta-Analyses) y el método de síntesis de datos propuesto por Webster y Watson (2002), este estudio analiza
28 artículos revisados por pares, centrándose en cómo se aplica la IA generativa en OCBA y cómo puede mejorar el rendi
miento de OCBA. Los resultados muestran que las redes generativas adversariales (GAN) son las más utilizadas, seguidas
de los autocodificadores variacionales (VAE) y los modelos autorregresivos. La revisión clasifica las áreas de aplicación de
la IA generativa en OCBA y examina cómo estas tecnologías mejoran la eficacia y la eficiencia de OCBA. Además, el ar
tículo analiza los retos asociados a la IA generativa, haciendo hincapié en la necesidad de tener en cuenta cuestiones éticas
como la parcialidad y la privacidad de los datos. Esta revisión contribuye a una comprensión más profunda del papel de la
IA generativa en la OCBA, esbozando sus aplicaciones y funcionalidades desde una perspectiva técnica.
Cómo citar
Kimura, . T. (2024). Explorando la frontera: Aplicaciones generativas de la IA en el análisis del comportamiento de los consumidores en línea. Cuadernos De Gestión, 1–14. https://doi.org/10.5295/cdg.232121tk
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Keywords
Inteligencia artificial generativa, Redes generativas adversariales, Autocodificadores variacionales, Modelo autorregresivo, Transformador generativo preentrenado, Análisis del comportamiento del consumidor online
References
Adebowale, O. J., & Agumba, J. N. (2022). Applications of augmented reality for construction productivity improvement: a systematic review. Smart and Sustainable Built Environment, (ahead-of-print). https://doi.org/10.1108/SASBE-06-2022-0128
Almahmood, R. J. K., & Tekerek, A. (2022). Issues and solutions in deep learning-enabled recommendation systems within the e-commerce field. Applied Sciences, 12(21), 11256. https://doi.org/10.3390/ app122111256
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019) Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101. https://doi.org/10.1016/j.aci.2018.05.004
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
Asperti, A., Evangelista, D., & Loli Piccolomini, E. (2021). A survey on variational autoencoders from a green AI perspective. SN Computer Science, 2(4), 301. https://doi.org/10.1007/s42979-021-00702-9
Baek, T. H. (2023). Digital advertising in the age of generative AI. Journal of Current Issues and Research in Advertising, 44(3), 249-251. https://doi.org/10.1080/10641734.2023.2243496
Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in e-commerce: A bibliometric study and literature review. Electronic Markets, 32(1), 297-338. https://doi.org/10.1007/ s12525-022-00537-z
Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2022). Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/TPAMI.2021.3116668
Breen, R. L. (2006). A practical guide to focus-group research. Journal of Geography in Higher Education, 30(3), 463-475. https://doi. org/10.1080/03098260600927575
Casanova, A., Careil, M., Romero-Soriano, A., Pal, C. J., Verbeek, J., & Drozdzal, M. (2023). Controllable image generation via collage representations. arXiv preprint arXiv:2304.13722.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953
Deshai, N., & Bhaskara Rao, B. (2023). Unmasking deception: A CNN and adaptive PSO approach to detecting fake online reviews. Soft Computing, 27(16), 11357-11378. https://doi.org/10.1007/s00500-023-08507-z
Ding, Y., Kang, W., Feng, J., Peng, B., & Yang, A. (2023). Credit card fraud detection based on improved Variational Autoencoder Generative Adversarial Network. IEEE Access, 11, 83680-83691. https://doi.org/10.1109/ACCESS.2023.3302339
Du, C., Yu, F., Jiang, M., Hua, A., Wei, X., Peng, T., & Hu, X. (2023). Vton-scfa: A virtual try-on network based on the semantic constraints and flow alignment. IEEE Transactions on Multimedia, 25, 777-791. https://doi.org/10.1109/TMM.2022.3152367
Fincato, M., Cornia, M., Landi, F., Cesari, F., & Cucchiara, R. (2022). Transform, warp, and dress: A new transformation-guided model for virtual try-on. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(2), 1-24. https://doi.org/10.1145/3491226
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448-455. https://doi.org/10.1016/j.ins.2017.12.030
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681-694. https://doi.org/10.1007/s11023-020-09548-1
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
Goti, A., Querejeta-Lomas, L., Almeida, A., de la Puerta, J. G., & López de-Ipiña, D. (2023). Artificial intelligence in business-to-customer fashion retail: A literature review. Mathematics, 11(13), 2943. https://doi.org/10.3390/math11132943
Grove, S. J., & Fisk, R. P. (1992). Observational data collection methods for services marketing: An overview. Journal of the Academy of Marketing Science, 20(3), 217-224. https://doi.org/10.1007/BF02723408
Guo, D., Liu, Z., & Li, R. (2023). RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection. Neural Networks, 166, 273-285. https://doi.org/10.1016/j.neunet.2023.07.026
Guo, J., Zheng, C., Li, S., Jia, Y., & Liu, B. (2022). BiInfGCN: Bilateral information augmentation of graph convolutional networks for recommendation. Mathematics, 10(17), 3042. https://doi.org/10.3390/math10173042
Han, Y., Nanda, G., & Moghaddam, M. (2023). Attribute-sentiment-guided summarization of user opinions from online reviews. Journal of Mechanical Design, 145(4), 041402. https://doi.org/10.1115/1.4055736
Hao, Y., Zhao, D., Li, H., Ip, W. H., & Liu, Y. (2022). User Behavior Prediction Based on DCGAN: The Case of Sina Weibo. Journal of Internet Technology, 23(6), 1367-1376. https://doi.org/10.53106/160792642022112306019
Hu, K., Qiu, L., Zhang, S., Wang, Z., Fang, N., & Zhou, H. (2023). A novel neighbor selection scheme based on dynamic evaluation towards recommender systems. Science Progress, 106(2), 368504231180090. https://doi.org/10.1177/00368504231180090
Jiang, L. A., Yang, Z., & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. Journal of Service Management, 24(2), 191-214. https://doi.org/10.1108/09564231311323962
Kashyap, D. A. K., Sahu, I., & Kumar, D. A. (2022). Artificial intelligence and its applications in e-commerce —A review analysis and research agenda. Journal of Theoretical and Applied Information Technology, 100(24), 7347-7465.
Kimura, T. (2022). Customer churn prediction with hybrid resampling and ensemble learning. Journal of Management Information & Decision Sciences, 25(1).
Kimura, T. (2023). Assessment of personal values for data-driven human resource management. Data Science Journal, 22. https://doi. org/10.5334/dsj-2023-022
Kmet, L., M., Lee, R., C., Cook, L., & S. (2004). Standard quality assessment criteria for evaluating primary research papers from a variety of fields. Alberta Heritage Foundation for Medical Research (Alberta Heritage Foundation for Medical Research).
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (26th ed). Springer.
Laenen, K., & Moens, M. F. (2022). Learning explainable disentangled representations of e-commerce data by aligning their visual and textual attributes. Computers, 11(12), 182. https://doi.org/10.3390/computers11120182
Li, J., Zhang, J., Bah, M. J., Wang, J., Zhu, Y., Yang, G., Li, L., & Zhang, K. (2022). An auto-encoder with genetic algorithm for high dimensional data: Towards accurate and interpretable outlier detection. Algorithms, 15(11), 429. https://doi.org/10.3390/a15110429
Li, K., Zhang, J., & Forsyth, D. (2023). POVNet: Image-based virtual try-on through accurate warping and residual. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12222-12235. https://doi.org/10.1109/TPAMI.2023.3283302
Lu, X., Liu, J., Gan, S., Li, T., Xiao, Y., & Liu, Y. (2022). Recommendation model based on dynamic interest group identification and data compensation. IEEE Transactions on Network and Service Management, 19(1), 89-99. https://doi.org/10.1109/TNSM.2021.3112702
Lungu, A. J., Swinkels, W., Claesen, L., Tu, P., Egger, J., & Chen, X. (2021). A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery. Expert Review of Medical Devices, 18(1), 47-62. https://doi.org/10.1080/17434440.2021.1860750
Margaris, D., Vassilakis, C., & Spiliotopoulos, D. (2022). On producing accurate rating predictions in sparse collaborative filtering datasets. Information, 13(6), 302. https://doi.org/10.3390/info13060302
Nabeel, M., Riaz, A., & Zhenyu, W. (2019). Cas-gans: An approach of dialogue policy learning based on gan and rl techniques. International Journal of Advanced Computer Science and Applications, 10(7). https://doi.org/10.14569/IJACSA.2019.0100766
Nallapati, R., Zhou, B., Gulcehre, C., & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023.
Necula, S. C., & Păvăloaia, V. (2023). AI-driven recommendations: A systematic review of the state of the art in e-commerce. Applied Sciences, 13(9), 5531. https://doi.org/10.3390/app13095531
Ngai, E. W., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35-48. https://doi.org/10.1016/j.jbusres.2022.02.049
Nguyen, M. D., & Cho, Y. S. (2020). A variational autoencoder mixture model for online behavior recommendation. IEEE Access, 8, 132736-132747. https://doi.org/10.1109/ACCESS.2020.3010508
Notin, P., Hernández-Lobato, J. M., & Gal, Y. (2021). Improving black-box optimization in VAE latent space using decoder uncertainty. Advances in Neural Information Processing Systems, 34, 802-814.
Oshrat, Y., Aumann, Y., Hollander, T., Maksimov, O., Ostroumov, A., Shechtman, N., & Kraus, S. (2022). Efficient customer service combining human operators and virtual agents. arXiv preprint arXiv:2209.05226.
Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019). Recent progress on generative adversarial networks (GANs): A survey. IEEE Access, 7, 36322-36333. https://doi.org/10.1109/ACCESS.2019.2905015
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. (2021). Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(1), 2617-2680.
Perez-Castro, A., Martínez-Torres, M. R., & Toral, S. L. (2023). Efficiency of automatic text generators for online review content generation. Technological Forecasting and Social Change, 189, 122380. https://doi.org/10.1016/j.techfore.2023.122380
Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., Stoffel, R. A., da Costa, C. A., Barbosa, J. L. V., ... & Arcot, T. (2021). Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review, 41, 100414. https://doi.org/10.1016/j.cosrev.2021.100414
Postolache, E., Mariani, G., Mancusi, M., Santilli, A., Cosmo, L., & Rodola, E. (2023). Latent autoregressive source separation. arXiv preprint arXiv:2301.08562.
Rabbi, M. F., Sultan, M. N., Hasan, M., & Islam, M. Z. (2023). Tribal dress identification using convolutional neural network. Journal of Information Hiding and Multimedia Signal Processing, 14(3), 72-80.
Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pretraining.
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews, 10, 1-19.
Roy, D., Santra, S., & Chanda, B. (2022). LGVTON: A landmark guided approach for model to person virtual try-on. Multimedia Tools and Applications, 81(4), 5051-5087. https://doi.org/10.1007/s11042-021-11647-9
Sajid, S., Rashid, R. M., & Haider, W. (2022). Changing trends of consumers’ online buying behavior during COVID-19 pandemic with moderating role of payment mode and gender. Frontiers in Psychology, 13, 919334. https://doi.org/10.3389/fpsyg.2022.919334
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229. https://doi.org/10.1147/rd.33.0210
Singh, M., Bajpai, U., V., V., & Prasath, S. (2020). Generation of fashionable clothes using generative adversarial networks: A preliminary feasibility study. International Journal of Clothing Science and Technology, 32(2), 177-187.
Sun, R., Dai, H., Li, L., Kearnes, S., & Dai, B. (2021). Towards understanding retrosynthesis by energy-based models. Advances in Neural Information Processing Systems, 34, 10186-10194.
Sun, Y., Wong, A. K. C., & Kamel, M. S. (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 23(4), 687-719. https://doi.org/10.1142/S0218001409007326
Terzioğlu, S., Çoğalmış, K. N., & Bulut, A. (2022). Ad creative generation using reinforced generative adversarial network. Electronic Commerce Research, 1-17. https://doi.org/10.1007/s10660-022-09564-6
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Viktoratos, I., & Tsadiras, A. (2021). Personalized advertising computational techniques: A systematic literature review, findings, and a design framework. Information, 12(11), 480. https://doi.org/10.3390/info12110480
Wang, S., & Yang, Y. (2021). M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores. Data Technologies and Applications, 55(5), 749-770. https://doi.org/10.1108/DTA-11-2020-0286
Wang, X. W., Chen, Y., & Liu, Y. Y. (2020). Link prediction through deep generative model. iScience, 23(10), 101626. https://doi.org/10.1016/j.isci.2020.101626
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, xiii-xxiii.
Wei, S., Zhou, X., An, X., Yang, X., & Xiao, Y. (2023). A heterogeneous e-commerce user alignment model based on data enhancement and data representation. Expert Systems with Applications, 228, 120258. https://doi.org/10.1016/j.eswa.2023.120258
Yang, Y., Ding, H., Liu, Q., & Quan, X. (2022). UBARv2: Towards mitigating exposure bias in task-oriented dialogs. arXiv preprint arXiv:2209.07239.
Yu, A., & Grauman, K. (2020). Densifying supervision for fine-grained visual comparisons. International Journal of Computer Vision, 128(10-11), 2704-2730. https://doi.org/10.1007/s11263-020-01344-9
Zhang, H., Sun, Y., Liu, L., & Xu, X. (2020). CascadeGAN: A category-supervised cascading generative adversarial network for clothes translation from the human body to tiled images. Neurocomputing. Cascade, 382, 148-161. https://doi.org/10.1016/j.neucom.2019.11.085
Zhang, H., Wang, X., Liu, L., Zhou, D., & Zhang, Z. (2020). Warpclothingout: A stepwise framework for clothes translation from the human body to tiled images. IEEE Multimedia, 27(4), 58-68. https://doi.org/10.1109/MMUL.2020.3014037
Zhang, H., Wong, R. K., & Chu, V. W. (2021). Hybrid variational autoencoder for recommender systems. ACM Transactions on Knowledge Discovery from Data, 16(2), 1-37. https://doi. org/10.1145/3470659
Zhang, X., Sun, Y., & Liu, L. (2021). An improved generative adversarial network for translating clothes from the human body to tiled image. Neural Computing and Applications, 33(14), 8445-8457. https://doi.org/10.1007/s00521-020-05598-9
Zhao, J., Li, J., Liu, H., Yan, S., & Feng, J. (2020). Fine-grained multi-human parsing. International Journal of Computer Vision, 128(8-9), 2185-2203. https://doi.org/10.1007/s11263-019-01181-5
Zhou, X., Han, X., Li, H., Wang, J., & Liang, X. (2022). Cross-domain image retrieval: Methods and applications. International Journal of Multimedia Information Retrieval, 11(3), 199-218. https://doi.org/10.1007/s13735-022-00244-7
Almahmood, R. J. K., & Tekerek, A. (2022). Issues and solutions in deep learning-enabled recommendation systems within the e-commerce field. Applied Sciences, 12(21), 11256. https://doi.org/10.3390/ app122111256
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019) Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101. https://doi.org/10.1016/j.aci.2018.05.004
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
Asperti, A., Evangelista, D., & Loli Piccolomini, E. (2021). A survey on variational autoencoders from a green AI perspective. SN Computer Science, 2(4), 301. https://doi.org/10.1007/s42979-021-00702-9
Baek, T. H. (2023). Digital advertising in the age of generative AI. Journal of Current Issues and Research in Advertising, 44(3), 249-251. https://doi.org/10.1080/10641734.2023.2243496
Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in e-commerce: A bibliometric study and literature review. Electronic Markets, 32(1), 297-338. https://doi.org/10.1007/ s12525-022-00537-z
Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2022). Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/TPAMI.2021.3116668
Breen, R. L. (2006). A practical guide to focus-group research. Journal of Geography in Higher Education, 30(3), 463-475. https://doi. org/10.1080/03098260600927575
Casanova, A., Careil, M., Romero-Soriano, A., Pal, C. J., Verbeek, J., & Drozdzal, M. (2023). Controllable image generation via collage representations. arXiv preprint arXiv:2304.13722.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953
Deshai, N., & Bhaskara Rao, B. (2023). Unmasking deception: A CNN and adaptive PSO approach to detecting fake online reviews. Soft Computing, 27(16), 11357-11378. https://doi.org/10.1007/s00500-023-08507-z
Ding, Y., Kang, W., Feng, J., Peng, B., & Yang, A. (2023). Credit card fraud detection based on improved Variational Autoencoder Generative Adversarial Network. IEEE Access, 11, 83680-83691. https://doi.org/10.1109/ACCESS.2023.3302339
Du, C., Yu, F., Jiang, M., Hua, A., Wei, X., Peng, T., & Hu, X. (2023). Vton-scfa: A virtual try-on network based on the semantic constraints and flow alignment. IEEE Transactions on Multimedia, 25, 777-791. https://doi.org/10.1109/TMM.2022.3152367
Fincato, M., Cornia, M., Landi, F., Cesari, F., & Cucchiara, R. (2022). Transform, warp, and dress: A new transformation-guided model for virtual try-on. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(2), 1-24. https://doi.org/10.1145/3491226
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448-455. https://doi.org/10.1016/j.ins.2017.12.030
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681-694. https://doi.org/10.1007/s11023-020-09548-1
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
Goti, A., Querejeta-Lomas, L., Almeida, A., de la Puerta, J. G., & López de-Ipiña, D. (2023). Artificial intelligence in business-to-customer fashion retail: A literature review. Mathematics, 11(13), 2943. https://doi.org/10.3390/math11132943
Grove, S. J., & Fisk, R. P. (1992). Observational data collection methods for services marketing: An overview. Journal of the Academy of Marketing Science, 20(3), 217-224. https://doi.org/10.1007/BF02723408
Guo, D., Liu, Z., & Li, R. (2023). RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection. Neural Networks, 166, 273-285. https://doi.org/10.1016/j.neunet.2023.07.026
Guo, J., Zheng, C., Li, S., Jia, Y., & Liu, B. (2022). BiInfGCN: Bilateral information augmentation of graph convolutional networks for recommendation. Mathematics, 10(17), 3042. https://doi.org/10.3390/math10173042
Han, Y., Nanda, G., & Moghaddam, M. (2023). Attribute-sentiment-guided summarization of user opinions from online reviews. Journal of Mechanical Design, 145(4), 041402. https://doi.org/10.1115/1.4055736
Hao, Y., Zhao, D., Li, H., Ip, W. H., & Liu, Y. (2022). User Behavior Prediction Based on DCGAN: The Case of Sina Weibo. Journal of Internet Technology, 23(6), 1367-1376. https://doi.org/10.53106/160792642022112306019
Hu, K., Qiu, L., Zhang, S., Wang, Z., Fang, N., & Zhou, H. (2023). A novel neighbor selection scheme based on dynamic evaluation towards recommender systems. Science Progress, 106(2), 368504231180090. https://doi.org/10.1177/00368504231180090
Jiang, L. A., Yang, Z., & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. Journal of Service Management, 24(2), 191-214. https://doi.org/10.1108/09564231311323962
Kashyap, D. A. K., Sahu, I., & Kumar, D. A. (2022). Artificial intelligence and its applications in e-commerce —A review analysis and research agenda. Journal of Theoretical and Applied Information Technology, 100(24), 7347-7465.
Kimura, T. (2022). Customer churn prediction with hybrid resampling and ensemble learning. Journal of Management Information & Decision Sciences, 25(1).
Kimura, T. (2023). Assessment of personal values for data-driven human resource management. Data Science Journal, 22. https://doi. org/10.5334/dsj-2023-022
Kmet, L., M., Lee, R., C., Cook, L., & S. (2004). Standard quality assessment criteria for evaluating primary research papers from a variety of fields. Alberta Heritage Foundation for Medical Research (Alberta Heritage Foundation for Medical Research).
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (26th ed). Springer.
Laenen, K., & Moens, M. F. (2022). Learning explainable disentangled representations of e-commerce data by aligning their visual and textual attributes. Computers, 11(12), 182. https://doi.org/10.3390/computers11120182
Li, J., Zhang, J., Bah, M. J., Wang, J., Zhu, Y., Yang, G., Li, L., & Zhang, K. (2022). An auto-encoder with genetic algorithm for high dimensional data: Towards accurate and interpretable outlier detection. Algorithms, 15(11), 429. https://doi.org/10.3390/a15110429
Li, K., Zhang, J., & Forsyth, D. (2023). POVNet: Image-based virtual try-on through accurate warping and residual. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12222-12235. https://doi.org/10.1109/TPAMI.2023.3283302
Lu, X., Liu, J., Gan, S., Li, T., Xiao, Y., & Liu, Y. (2022). Recommendation model based on dynamic interest group identification and data compensation. IEEE Transactions on Network and Service Management, 19(1), 89-99. https://doi.org/10.1109/TNSM.2021.3112702
Lungu, A. J., Swinkels, W., Claesen, L., Tu, P., Egger, J., & Chen, X. (2021). A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery. Expert Review of Medical Devices, 18(1), 47-62. https://doi.org/10.1080/17434440.2021.1860750
Margaris, D., Vassilakis, C., & Spiliotopoulos, D. (2022). On producing accurate rating predictions in sparse collaborative filtering datasets. Information, 13(6), 302. https://doi.org/10.3390/info13060302
Nabeel, M., Riaz, A., & Zhenyu, W. (2019). Cas-gans: An approach of dialogue policy learning based on gan and rl techniques. International Journal of Advanced Computer Science and Applications, 10(7). https://doi.org/10.14569/IJACSA.2019.0100766
Nallapati, R., Zhou, B., Gulcehre, C., & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023.
Necula, S. C., & Păvăloaia, V. (2023). AI-driven recommendations: A systematic review of the state of the art in e-commerce. Applied Sciences, 13(9), 5531. https://doi.org/10.3390/app13095531
Ngai, E. W., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35-48. https://doi.org/10.1016/j.jbusres.2022.02.049
Nguyen, M. D., & Cho, Y. S. (2020). A variational autoencoder mixture model for online behavior recommendation. IEEE Access, 8, 132736-132747. https://doi.org/10.1109/ACCESS.2020.3010508
Notin, P., Hernández-Lobato, J. M., & Gal, Y. (2021). Improving black-box optimization in VAE latent space using decoder uncertainty. Advances in Neural Information Processing Systems, 34, 802-814.
Oshrat, Y., Aumann, Y., Hollander, T., Maksimov, O., Ostroumov, A., Shechtman, N., & Kraus, S. (2022). Efficient customer service combining human operators and virtual agents. arXiv preprint arXiv:2209.05226.
Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019). Recent progress on generative adversarial networks (GANs): A survey. IEEE Access, 7, 36322-36333. https://doi.org/10.1109/ACCESS.2019.2905015
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. (2021). Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(1), 2617-2680.
Perez-Castro, A., Martínez-Torres, M. R., & Toral, S. L. (2023). Efficiency of automatic text generators for online review content generation. Technological Forecasting and Social Change, 189, 122380. https://doi.org/10.1016/j.techfore.2023.122380
Policarpo, L. M., da Silveira, D. E., da Rosa Righi, R., Stoffel, R. A., da Costa, C. A., Barbosa, J. L. V., ... & Arcot, T. (2021). Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review, 41, 100414. https://doi.org/10.1016/j.cosrev.2021.100414
Postolache, E., Mariani, G., Mancusi, M., Santilli, A., Cosmo, L., & Rodola, E. (2023). Latent autoregressive source separation. arXiv preprint arXiv:2301.08562.
Rabbi, M. F., Sultan, M. N., Hasan, M., & Islam, M. Z. (2023). Tribal dress identification using convolutional neural network. Journal of Information Hiding and Multimedia Signal Processing, 14(3), 72-80.
Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pretraining.
Rethlefsen, M. L., Kirtley, S., Waffenschmidt, S., Ayala, A. P., Moher, D., Page, M. J., & Koffel, J. B. (2021). PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews, 10, 1-19.
Roy, D., Santra, S., & Chanda, B. (2022). LGVTON: A landmark guided approach for model to person virtual try-on. Multimedia Tools and Applications, 81(4), 5051-5087. https://doi.org/10.1007/s11042-021-11647-9
Sajid, S., Rashid, R. M., & Haider, W. (2022). Changing trends of consumers’ online buying behavior during COVID-19 pandemic with moderating role of payment mode and gender. Frontiers in Psychology, 13, 919334. https://doi.org/10.3389/fpsyg.2022.919334
Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3), 210-229. https://doi.org/10.1147/rd.33.0210
Singh, M., Bajpai, U., V., V., & Prasath, S. (2020). Generation of fashionable clothes using generative adversarial networks: A preliminary feasibility study. International Journal of Clothing Science and Technology, 32(2), 177-187.
Sun, R., Dai, H., Li, L., Kearnes, S., & Dai, B. (2021). Towards understanding retrosynthesis by energy-based models. Advances in Neural Information Processing Systems, 34, 10186-10194.
Sun, Y., Wong, A. K. C., & Kamel, M. S. (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 23(4), 687-719. https://doi.org/10.1142/S0218001409007326
Terzioğlu, S., Çoğalmış, K. N., & Bulut, A. (2022). Ad creative generation using reinforced generative adversarial network. Electronic Commerce Research, 1-17. https://doi.org/10.1007/s10660-022-09564-6
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Viktoratos, I., & Tsadiras, A. (2021). Personalized advertising computational techniques: A systematic literature review, findings, and a design framework. Information, 12(11), 480. https://doi.org/10.3390/info12110480
Wang, S., & Yang, Y. (2021). M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores. Data Technologies and Applications, 55(5), 749-770. https://doi.org/10.1108/DTA-11-2020-0286
Wang, X. W., Chen, Y., & Liu, Y. Y. (2020). Link prediction through deep generative model. iScience, 23(10), 101626. https://doi.org/10.1016/j.isci.2020.101626
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, xiii-xxiii.
Wei, S., Zhou, X., An, X., Yang, X., & Xiao, Y. (2023). A heterogeneous e-commerce user alignment model based on data enhancement and data representation. Expert Systems with Applications, 228, 120258. https://doi.org/10.1016/j.eswa.2023.120258
Yang, Y., Ding, H., Liu, Q., & Quan, X. (2022). UBARv2: Towards mitigating exposure bias in task-oriented dialogs. arXiv preprint arXiv:2209.07239.
Yu, A., & Grauman, K. (2020). Densifying supervision for fine-grained visual comparisons. International Journal of Computer Vision, 128(10-11), 2704-2730. https://doi.org/10.1007/s11263-020-01344-9
Zhang, H., Sun, Y., Liu, L., & Xu, X. (2020). CascadeGAN: A category-supervised cascading generative adversarial network for clothes translation from the human body to tiled images. Neurocomputing. Cascade, 382, 148-161. https://doi.org/10.1016/j.neucom.2019.11.085
Zhang, H., Wang, X., Liu, L., Zhou, D., & Zhang, Z. (2020). Warpclothingout: A stepwise framework for clothes translation from the human body to tiled images. IEEE Multimedia, 27(4), 58-68. https://doi.org/10.1109/MMUL.2020.3014037
Zhang, H., Wong, R. K., & Chu, V. W. (2021). Hybrid variational autoencoder for recommender systems. ACM Transactions on Knowledge Discovery from Data, 16(2), 1-37. https://doi. org/10.1145/3470659
Zhang, X., Sun, Y., & Liu, L. (2021). An improved generative adversarial network for translating clothes from the human body to tiled image. Neural Computing and Applications, 33(14), 8445-8457. https://doi.org/10.1007/s00521-020-05598-9
Zhao, J., Li, J., Liu, H., Yan, S., & Feng, J. (2020). Fine-grained multi-human parsing. International Journal of Computer Vision, 128(8-9), 2185-2203. https://doi.org/10.1007/s11263-019-01181-5
Zhou, X., Han, X., Li, H., Wang, J., & Liang, X. (2022). Cross-domain image retrieval: Methods and applications. International Journal of Multimedia Information Retrieval, 11(3), 199-218. https://doi.org/10.1007/s13735-022-00244-7
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