Fidelio, pipeline hybrid movie recommender system

The Pipeline Architecture

🔹 Pipeline: The Gradient Boosted Hybrid

1. Content Encoding: Metadata is encoded into numerical features.
2. Collaborative Features: User and Item embeddings/vectors are used as input features.
3. Gradient Boosting Machine (GBM): A Tree-based model (Hist Gradient Boosting) is trained on these combined features to predict the specific user rating.

Data Sources & Acknowledgments

• MovieLens Dataset
  User–item ratings are derived from the MovieLens datasets provided by GroupLens Research.
  These ratings form the backbone of the collaborative filtering pipeline.
  • https://grouplens.org/datasets/movielens/
• The Movie Database (TMDB) API
  Rich movie metadata (TMDB IDs, cast, crew, directors, genres) was collected programmatically via the TMDB API and used primarily for content-based modeling.
  • https://developers.themoviedb.org/3
• Rotten Tomatoes Reviews Dataset
  A large-scale dataset of critic and audience reviews scraped from Rotten Tomatoes, used to enrich item representations and provide additional signals for cold-start movies and sentiment-aware modeling.
  • https://www.kaggle.com/datasets/priyamchoksi/rotten-tomato-movie-reviews-1-44m-rows

Trained Model