Understanding the Role of AI in Personalized Recommendation Systems, Applications, Concepts, and Algorithms |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Authors : Shrikaa Jadiga | ||
| DOI : 10.14445/22312803/IJCTT-V73I1P113 | ||
How to Cite?
Shrikaa Jadiga, "Understanding the Role of AI in Personalized Recommendation Systems, Applications, Concepts, and Algorithms," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 106-118, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P113
Abstract
Personalized suggestion systems integrate Artificial Intelligence (AI) towards a revolutionized customer experience, interaction, and performance within the digital space. Enhancing AI algorithms is essential to support big data analytics and decision-making among users within an online digital space, supporting pattern identification, personalized suggestions, and experience, ensuring tailored customer experience and involvement. The paper focuses on AI's underpinning contribution and role in supporting and transforming myriad domains such as finance, healthcare, entertainment, and education. The AI-based platform contributes to overall outcomes in such platforms through techniques of filtering, content-based filtering, and hybrid approaches to predict user preferences accurately. Recent advancements in machine learning, particularly deep learning, have further enhanced these systems by enabling a more nuanced understanding and prediction of user behavior. With such advancement, there is real-time system personalization, triggering improvements in user experience and business profitability. Implementing AI-based system recommendations accommodates undeniable challenges, including data privacy, lack of transparency, and algorithmic bias, prompting the need to address ethical issues to achieve undeniable user trust and experiences. Similarly, the paper addresses underpinning solutions toward sustainable AI-based personalized systems through explainable AI techniques, robust data governance techniques, and algorithm-based mitigation strategies as long-term solutions. AI-based personalization systems accommodate trends such as integrating multi-modal data sources and using contextual signals to provide even more personalized recommendations. Trends and Innovation create a positive platform to enhance recommendation system effectiveness and challenges management within an AI application and outcomes. An in depth analysis of applications, concepts, and algorithms applicable in personalized recommendation systems is vital because the areas give a deeper comprehension of AI applications and integration of seamless user experience and interactions, stressing the significance of creating a tradeoff between Innovation and ethical implications. The journal contributes to a growing body of Artificial Intelligence and transformative aspects in personalizing digital experience, noting opportunities and challenges within an evolving digital space and applications.
Keywords
Algorithms, Applications, Artificial Intelligence Recommendation Systems.
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