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This research paper explores “The Movies Dataset” to build an effective movie recommendation system. The dataset includes various attributes like genre, ratings, cast, and user preferences. Our primary objective is to find the best ensemble model that combines singular value decomposition (SVD), SVD +  + (SVD with implicit feedback), and KNN baseline (k-nearest neighbors with baseline) recommendation algorithms to improve prediction accuracy and recommendation quality. The rapid expansion of digital media platforms has generated vast data in the entertainment industry, making movie recommendation systems crucial for enhancing user experience. Through our study, we aim to contribute to the advancement of personalized movie suggestions and user satisfaction in the digital entertainment domain.

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An Efficient Movie Recommendation Model Using Machine Learning-Based Ensemble Model

  • 会议论文

  • 发表日期：

    2025年

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  • Reema Goyal,
  • Darpan Anand,
  • Loveleena Mukhija,
  • Sonam Juneja,
  • Shikha Atwal

摘要

This research paper explores “The Movies Dataset” to build an effective movie recommendation system. The dataset includes various attributes like genre, ratings, cast, and user preferences. Our primary objective is to find the best ensemble model that combines singular value decomposition (SVD), SVD +  + (SVD with implicit feedback), and KNN baseline (k-nearest neighbors with baseline) recommendation algorithms to improve prediction accuracy and recommendation quality. The rapid expansion of digital media platforms has generated vast data in the entertainment industry, making movie recommendation systems crucial for enhancing user experience. Through our study, we aim to contribute to the advancement of personalized movie suggestions and user satisfaction in the digital entertainment domain.