Movie Recommendation System

This project implements a collaborative filtering recommendation system using TensorFlow and Keras.
The model predicts user ratings for movies and recommends unseen movies with the highest predicted scores.

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Project structure

• 01_process_data.ipynb – Preprocessing of the raw data (cleaning, merging, saving ratings_meta_small.csv).
• 02_train_model.ipynb – Training the recommendation model using embeddings, concatenation, dense layers, and a linear output.
• 03_recommend_system.ipynb – Using the trained model to generate movie recommendations for a given user.
• requirements.txt – Python dependencies.
• report_swedish.pdf – Project report describing the system and results. (Swedish)

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How to Run

1. Clone the repo and move into the folder

   git clone https://github.com/melkerliljegren/movie-recommender.git
   cd movie-recommender
   

2. Install required Python packages

   pip install -r requirements.txt
   

3. Open the .ipynb files in VS Code and run the cells.