A COMPREHENSIVE FRAMEWORK FOR INTEGRATING AI AND MACHINE LEARNING IN PERSONALIZATION AND AD TARGETING WITHIN E-COMMERCE
Keywords:
Personalization, Ad Targeting, Machine Learning, Data Science, Artificial Intelligence, E-commerce, Consumer ExperienceAbstract
This paper delves deep into the influential role of Artificial Intelligence (AI) and Machine Learning (ML) in the progressive evolution of personalization strategies in e-commerce and ad targeting. It starts with a broad examination of the functions of AI and ML across various sectors, underlining their expanding convergence with the domains of e-commerce and advertising. As we witness a reshaping of e-commerce personalization through AI, we delve into particular techniques that utilize AI to amplify the online shopping experience. This study further delves into the transformative impact of machine learning in refining ad targeting techniques. We also present a detailed roadmap that outlines a practical methodology for enterprises seeking to incorporate AI and ML in personalizing e-commerce experiences and optimizing ad targeting strategies. Further, we reflect on the ethical implications bound with this technological revolution, addressing privacy concerns, the impacts on consumer behavior, and the importance of regulatory compliance. In the concluding part, we ponder the future course of AI and ML in e-commerce personalization and ad targeting. While acknowledging some challenges, it becomes apparent that AI and ML are significantly transforming these fields, assuring a more personalized and efficient consumer experience, while also highlighting critical ethical considerations. This all-encompassing review offers a valuable guide to understand the prevailing landscape and foresee emerging trends.
References
Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., ... & Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69-76.
Lee, K. H. (2012). The influences of the just-in-time social cloud on real world decisions (Doctoral dissertation, Massachusetts Institute of Technology).
Rahman, M. M., Abresham, A. E., Pranto, A. J., & Rahman, M. R. (2023). Al and Machine Learning Applications to Enhance Customer Support Md Shamim Hossain ID https://orcid. org/0000-0003-1645-7470. Handbook of Research on AI and Machine Learning Applications in Customer Support and Analytics, 300.
Mnyakin, M. (2020). Investigating the Impacts of AR, AI, and Website Optimization on Ecommerce Sales Growth. ResearchBerg Review of Science and Technology, 3(1), 116-130.
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.
Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data mining and knowledge discovery, 5, 115-153
Li, W., & Huang, Z. (2016). The research of influence factors of online behavioral advertising avoidance. American Journal of Industrial and Business Management, 6(09), 947.
Estrada-Jiménez, J., Parra-Arnau, J., Rodríguez-Hoyos, A., & Forné, J. (2017). Online advertising: Analysis of privacy threats and protection approaches. Computer Communications, 100, 32-51.
Aïmeur, E., & Tremblay, A. (2018, July). Me, myself and i: Are looking for a balance between personalization and privacy. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 115-119).
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Rudrendu Kumar Paul, Aryyama Kumar Jana (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
