Abstract
<jats:p>The proliferation of artificial intelligence (AI) in mobile applications has created a growing demand for highly personalized user experiences. However, a fundamental challenge persists: the "cold-start" problem, where new users or newly installed apps lack sufficient interaction data to train effective personalization models. Simultaneously, the modern user engages with a diverse ecosystem of apps, generating fragmented, platform-specific behavioral data. This research investigates Transfer Learning as a strategic solution for achieving cross-platform personalization, enabling AI models to leverage knowledge from a user's established behavior on one app (the source domain) to enhance personalization in a new or different app (the target domain). This study proposes a novel framework for deep transfer learning that addresses the inherent challenges of heterogeneous feature spaces, varying interaction semantics, and distinct user intent across different mobile platforms. The framework integrates advanced techniques such as domain-adversarial neural networks to learn domain-invariant user representations and meta-learning strategies for rapid adaptation to new app environments with minimal fine-tuning data. We explore various transfer scenarios, including knowledge transfer between apps of similar categories (e.g., social media to social media) and more complex transfers between dissimilar categories (e.g., a music streaming app to a news app), underpinned by shared user attributes and temporal interaction patterns.</jats:p>