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Personalized activity recommendation systems are increasingly valued for aligning with individual needs and preferences. This research presents a generative language model-based system that delivers tailored activity suggestions across diverse age groups, including neuro diverse individuals, employees, children, seniors, and students. Using a custom dataset capturing behavioral traits, the system fine-tunes the BART model to generate recommendations that address individual weaknesses. Unlike traditional systems, it adapts to multiple user characteristics, enhancing relevance and user engagement. The evaluation results show improved satisfaction and participation rates, demonstrating the potential of the model to effectively serve a broad and diverse audience.

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A Generative Language Model-Based System for Personalized Activity Recommendation Across Diverse Age Groups

  • 会议论文

  • 发表日期：

    2026年

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  • Nidhi,
  • Ajay Malviya,
  • Sanjivani Adsul,
  • Dnyaneshwari Jagdale,
  • Rujuta Joshi,
  • Ayush Joshi,
  • Aditi Kharche,
  • Saurabh Kadam

摘要

Personalized activity recommendation systems are increasingly valued for aligning with individual needs and preferences. This research presents a generative language model-based system that delivers tailored activity suggestions across diverse age groups, including neuro diverse individuals, employees, children, seniors, and students. Using a custom dataset capturing behavioral traits, the system fine-tunes the BART model to generate recommendations that address individual weaknesses. Unlike traditional systems, it adapts to multiple user characteristics, enhancing relevance and user engagement. The evaluation results show improved satisfaction and participation rates, demonstrating the potential of the model to effectively serve a broad and diverse audience.