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item-based-recommendation

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usc-dsci553-data-mining-sp24

Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.

  • Updated Nov 12, 2023
  • Jupyter Notebook

TMDB_5000_Movie_recommendation_system is a repository for a hybrid movie recommendation system. Discover personalized movie recommendations based on user preferences and movie features using the TMDB 5000 Movies dataset.

  • Updated Apr 20, 2023
  • Jupyter Notebook

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