Generative Models for Context-Aware Recommender Systems: A Survey

Recommender systems have become integral to personalized content delivery, with deep learning (DL) techniques substantially improving their accuracy and scalability. We examine the integration of generative models into context-aware recommender systems, addressing challenges related to dynamic, partially observable, and latent user contexts. Moreover, we present foundational definitions of context, strategies for incorporating contextual cues, and the role of sequential and interactive generative systems in enhancing recommendation quality. Finally, we explore how generative models enable richer representations of user behaviors and facilitate generating and evaluating contextually relevant recommendations.