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Abstract—This review analyzes media recommendation systems for music, movies, books, and activities. Traditional systems focus on user preferences, genre similarity, and collaborative filtering, often overlooking emotional impact and personalization. We explore using reinforcement learning and egocentric networks to enhance user experience through emotional engagement and social context. It aims to improve personalization and relevance, addressing traditional systems’ limitations. The findings highlight the potential of these approaches to offer more personalized and emotionally attuned media recommendations.

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Reinforcement Learning Based Activity Recommendation for Providing Emotional Support

  • 会议论文

  • 发表日期：

    2026年

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  • Yasodha Vimukthi,
  • M.K.N.M. Kodituwakku,
  • J.A.S. Nimnadi,
  • K.N.I. Premathilaka,
  • Dharshana Kasthurirathna

摘要

Abstract—This review analyzes media recommendation systems for music, movies, books, and activities. Traditional systems focus on user preferences, genre similarity, and collaborative filtering, often overlooking emotional impact and personalization. We explore using reinforcement learning and egocentric networks to enhance user experience through emotional engagement and social context. It aims to improve personalization and relevance, addressing traditional systems’ limitations. The findings highlight the potential of these approaches to offer more personalized and emotionally attuned media recommendations.