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An Overview of AI-driven Recommendation Systems: Enhancing Personalization & User Experience (Qualitative Study)
Corresponding Author(s) : Dragoș-Cătălin PAHONȚU
Student Thinkers and Advanced Research,
Vol. 3 No. 2 (2024): Proceedings of the 7th International Conference XGEN
Abstract
This study provides a comprehensive examination of AI-driven recommendation systems, acknowledging their multidisciplinary nature and the various academic perspectives that contribute to understanding their complexity. With a focus on the retail sector, the research investigates user attitudes towards the increasingly personalized consumer environment, particularly regarding privacy concerns, and examines the consequent impact on customer experiences.
Employing a qualitative research methodology, this study examines existing AI recommendation systems through a comprehensive literature review and in-depth case
and Amazon, the paper highlights the substantial economic benefits derived from AI personalization. Drawing insights from industry examples, including the infamous Facebook Beacon controversy, the research underscores the fine balance between leveraging personal data for customization and maintaining user privacy. The paper offers a balanced perspective on the advantages of AI in recommendation systems while highlighting the urgent need for ethical frameworks to govern their use.
The findings indicate that advanced personalization techniques contribute to enhanced user satisfaction and improved business outcomes. However, these systems also pose challenges, especially in the realm of user privacy and data ethics.
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- Alimamy, S., & Gnoth, J. (2022, March 1). I want it my way! The effect of perceptions of personalization through augmented reality and online shopping on customer intentions to co-create value. , 128, 107105-107105. https://www.sciencedirect.com/science/article/pii/S0747563221004283
- Batchelor, N., Houston, D., Larson, S., Nanjundaram, S., & Vota, W. (2014, May 19). Facebook Case Study. https://www.scribd.com/document/225029977/Facebook-Beacon-Case-Study
- Chevalier, S. (2023, September 28). Ways in which companies measure the success of using artificial intelligence (AI)-driven personalization worldwide 2023. Retrieved March 23, 2024 from https://www.statista.com/statistics/1415821/success-measurement-in-using-ai-driven-personalization-worldwide-2023/#statisticContainer
- Gomez-Uribe, C A., & Hunt, N T. (2015, December 28). The Netflix Recommender System. https://doi.org/10.1145/2843948
- MacKenzie, I., Meyer, C., & Noble, S. (2013, October 1). How Retailers can Keep up With Consumers. Retrieved March 9, 2024 from https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers#/
- Pillai, R., Sivathanu, B., & Dwivedi, Y K. (2020, November 1). Shopping intention at AI-powered automated retail stores (AIPARS). Elsevier BV, 57, 102207-102207. https://doi.org/10.1016/j.jretconser.2020.102207
- Robinson, S G., Orsingher, C., Alkire, L., Keyser, A D., Giebelhausen, M., Papamichail, K N., Shams, P., & Temerak, M S. (2020, August 1). Frontline encounters of the AI kind: An evolved service encounter framework. Elsevier BV, 116, 366-376. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.08.038
- Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J L., Bull, J., & Waddoups, R. (2021, March 1). How Technology is Changing Retail. Elsevier BV, 97(1), 13-27. https://doi.org/10.1016/j.jretai.2020.10.006
- Smith, B., & Linden, G. (2017, May 15). Two Decades of Recommender Systems at Amazon.com
- Soltanifar, M., Hughes, M., & Göcke, L. (2021, January 1). Digital Entrepreneurship. Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-030-53914-6
- Toch, E., Wang, Y., & Cranor, L F. (2012, March 10). Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. https://doi.org/10.1007/s11257-011-9110-z
References
Alimamy, S., & Gnoth, J. (2022, March 1). I want it my way! The effect of perceptions of personalization through augmented reality and online shopping on customer intentions to co-create value. , 128, 107105-107105. https://www.sciencedirect.com/science/article/pii/S0747563221004283
Batchelor, N., Houston, D., Larson, S., Nanjundaram, S., & Vota, W. (2014, May 19). Facebook Case Study. https://www.scribd.com/document/225029977/Facebook-Beacon-Case-Study
Chevalier, S. (2023, September 28). Ways in which companies measure the success of using artificial intelligence (AI)-driven personalization worldwide 2023. Retrieved March 23, 2024 from https://www.statista.com/statistics/1415821/success-measurement-in-using-ai-driven-personalization-worldwide-2023/#statisticContainer
Gomez-Uribe, C A., & Hunt, N T. (2015, December 28). The Netflix Recommender System. https://doi.org/10.1145/2843948
MacKenzie, I., Meyer, C., & Noble, S. (2013, October 1). How Retailers can Keep up With Consumers. Retrieved March 9, 2024 from https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers#/
Pillai, R., Sivathanu, B., & Dwivedi, Y K. (2020, November 1). Shopping intention at AI-powered automated retail stores (AIPARS). Elsevier BV, 57, 102207-102207. https://doi.org/10.1016/j.jretconser.2020.102207
Robinson, S G., Orsingher, C., Alkire, L., Keyser, A D., Giebelhausen, M., Papamichail, K N., Shams, P., & Temerak, M S. (2020, August 1). Frontline encounters of the AI kind: An evolved service encounter framework. Elsevier BV, 116, 366-376. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.08.038
Shankar, V., Kalyanam, K., Setia, P., Golmohammadi, A., Tirunillai, S., Douglass, T., Hennessey, J L., Bull, J., & Waddoups, R. (2021, March 1). How Technology is Changing Retail. Elsevier BV, 97(1), 13-27. https://doi.org/10.1016/j.jretai.2020.10.006
Smith, B., & Linden, G. (2017, May 15). Two Decades of Recommender Systems at Amazon.com
Soltanifar, M., Hughes, M., & Göcke, L. (2021, January 1). Digital Entrepreneurship. Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-030-53914-6
Toch, E., Wang, Y., & Cranor, L F. (2012, March 10). Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. https://doi.org/10.1007/s11257-011-9110-z