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Abstract <p>In the rapidly evolving landscape of digital content consumption, the demand for sophisticated movie recommendation systems is more pronounced than ever. This study introduces “Cinescope,” a pioneering recommendation system employing a spectrum of cutting-edge machine learning models—SVD collaborative filtering, KNN collaborative filtering, content-based filtering (text and image), hybrid (LightFM), attention, and ensemble models—signifying a significant advancement in tailoring recommendations to individual preferences. This research not only transcends theoretical frameworks but also highlights the significant impact of merging various machine-learning approaches, providing important insights into the collaborative benefits of integrating recommendation models. The success of “Cinescope” marks a pivotal contribution to the ongoing evolution of personalized streaming services and sets new standards for the integration of machine learning in reshaping viewer interaction with digital content.</p>

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Cinescope: An Intelligent Context-Aware Movie Recommendation System for Enhancing Viewer Experience

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Kulvinder Singh,
  • Sanjeev Dhawan,
  • Manoj Yadav

摘要

Abstract

In the rapidly evolving landscape of digital content consumption, the demand for sophisticated movie recommendation systems is more pronounced than ever. This study introduces “Cinescope,” a pioneering recommendation system employing a spectrum of cutting-edge machine learning models—SVD collaborative filtering, KNN collaborative filtering, content-based filtering (text and image), hybrid (LightFM), attention, and ensemble models—signifying a significant advancement in tailoring recommendations to individual preferences. This research not only transcends theoretical frameworks but also highlights the significant impact of merging various machine-learning approaches, providing important insights into the collaborative benefits of integrating recommendation models. The success of “Cinescope” marks a pivotal contribution to the ongoing evolution of personalized streaming services and sets new standards for the integration of machine learning in reshaping viewer interaction with digital content.