ml_recommendations

Personalized Plex recommendations are generated by importing your watch history and training a machine-learning model on your viewing preferences. The system analyzes past views to produce ranked movie and TV show suggestions tailored to your tastes.

Yes, you can automatically train a recommender using Plex viewing history. The auto_train_from_plex function imports your watch data and trains the model automatically to keep suggestions up-to-date without manual intervention.

Yes, generating Plex recommendations requires a Python environment with machine-learning libraries installed. This setup is necessary to process your watch history data, train the ranking model, and output personalized viewing suggestions.

You record new viewing events using the record_viewing function to update your recommendation model. This adds the latest watched movies and TV shows to the dataset, ensuring the machine-learning model adapts to your current preferences.

The best way to rank shows based on your Plex watch history is using the recommend_content function. It applies a trained machine-learning model to your viewing data to output a ranked list of personalized movie and TV show suggestions.

A limitation when importing Plex history for recommendations is model performance dependency on data volume. You can import the last N watched items, such as 50, to train the recommender, but larger datasets may require more processing time and robust Python environments.