Reinforcement learning enabled Egocentric social network based activity recommendation system for providing emotional support

Here we present a comprehensive analysis of research on media recommendation systems, encompassing music, movies, books… etc. Traditional recommender systems primarily focus on factors such as user preferences, genre similarity, and collaborative filtering, often neglecting the emotional impact and personalization of media on users. To address this limitation, this paper explores the integration of reinforcement learning and egocentric networks to improve user experience through emotional engagement and social context. The study aims to enhance the personalization and relevance of recommendations, addressing the limitations of traditional systems and paving the way for more emotionally and socially attuned media suggestions. The findings of this review highlight the potential of RL and egocentric networks in improving media recommendation systems and providing users with more personalized and emotionally attuned media suggestions.

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