A Brief Review on Audiobook Recommendation System Based on Contextual and Emotional Cues

The exponential growth of audio book platforms has resulted in an overwhelming volume of content, necessitating intelligent recommendation systems that go beyond traditional filtering approaches. Conventional collaborative and content-based filtering methods often overlook critical aspects such as user emotions and real-time contextual factors, leading to suboptimal personalization. This survey presents a comprehensive review of recent developments in audio book recommendation systems that incorporate hybrid deep learning models, emotion recognition, and context- awareness. It evaluates various techniques, including sentiment analysis using natural language processing (NLP), collaborative-content hybridization, and multimodal emotion modeling. The paper also outlines the limitations of existing systems, such as the cold start problem, limited emotional input modalities, and privacy concerns. A detailed research gap analysis is conducted to highlight future opportunities in this domain. The survey aims to serve as a foundation for researchers and practitioners working toward emotionally adaptive,context-driven recommendation engines for audiobook and media platforms.