AI-Driven Personalisation: Transforming User Experience Across Mobile Applications

<p>AI-driven personalisation represents a transformative innovation in mobile application development, revolutionising user engagement across domains such as e-commerce, social media, education and healthcare. By leveraging cutting-edge machine learning (ML) and deep learning frameworks, these systems deliver real-time, context-aware and user-specific recommendations, significantly enhancing user interaction, retention and satisfaction. This study provides a systematic review of foundational methodologies, including collaborative filtering, deep neural networks and transformer-based architectures, examining their application across diverse industries. A particular focus is placed on multimodal and context-aware approaches that underpin adaptive, scalable and privacy-conscious solutions.</p>

Authors

Publication Details

Journal
Journal of Artificial Intelligence Machine Learning and Data Science
Published
2025-01-10
DOI
https://doi.org/10.51219/jaimld/maxwell-nwanna/425
Citations
11
Primary Topic
Personal Information Management and User Behavior
Type
article
Field-Weighted Citation Impact
45.86
Controls
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article

AI-Driven Personalisation: Transforming User Experience Across Mobile Applications

Adeseye Ifaturoti, Oluwole Fagbohun, Maxwell Nwanna, Ernest Effiong Offiong et al.
11 citations
Journal of Artificial Intelligence Machine Learning and Data Science
Personal Information Management and User Behavior
45.86
article

AI-Driven Personalisation: Transforming User Experience Across Mobile Applications

Adeseye Ifaturoti, Oluwole Fagbohun, Maxwell Nwanna, Ernest Effiong Offiong, Toluwaleke Ogidan, Oladotun Fasogbon
article en
11 citations

Abstract

<p>AI-driven personalisation represents a transformative innovation in mobile application development, revolutionising user engagement across domains such as e-commerce, social media, education and healthcare. By leveraging cutting-edge machine learning (ML) and deep learning frameworks, these systems deliver real-time, context-aware and user-specific recommendations, significantly enhancing user interaction, retention and satisfaction. This study provides a systematic review of foundational methodologies, including collaborative filtering, deep neural networks and transformer-based architectures, examining their application across diverse industries. A particular focus is placed on multimodal and context-aware approaches that underpin adaptive, scalable and privacy-conscious solutions.</p>

Journal of Artificial Intelligence Machine Learning and Data ScienceVol. 3(1)
Openalex Percentile: Top 0%
Personal Information Management and User Behavior
45.86
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