https://github.com/richmonddjwerter/autoencoder-based-multi-modal-movie-recommendation-system
This Multi-Modal Movie Recommendation System leverages a combination of structured numerical features and deep text embeddings to provide accurate and personalized movie recommendations. Unlike traditional recommender systems that rely solely on user ratings or metadata, this model integrates numerical attributes (such as popularity and ratings)
https://github.com/richmonddjwerter/autoencoder-based-multi-modal-movie-recommendation-system
ai all-minilm-l6-v2 autoencoder-neural-network cosine-similarity deep-neural-networks embedding-cache gradio imdb-dataset machine-learning-algorithms movie-recomendation-system python3 standardscaler
Last synced: 18 days ago
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This Multi-Modal Movie Recommendation System leverages a combination of structured numerical features and deep text embeddings to provide accurate and personalized movie recommendations. Unlike traditional recommender systems that rely solely on user ratings or metadata, this model integrates numerical attributes (such as popularity and ratings)
- Host: GitHub
- URL: https://github.com/richmonddjwerter/autoencoder-based-multi-modal-movie-recommendation-system
- Owner: RichmondDjwerter
- Created: 2025-04-13T12:10:26.000Z (19 days ago)
- Default Branch: main
- Last Pushed: 2025-04-13T12:20:37.000Z (19 days ago)
- Last Synced: 2025-04-15T01:17:52.152Z (18 days ago)
- Topics: ai, all-minilm-l6-v2, autoencoder-neural-network, cosine-similarity, deep-neural-networks, embedding-cache, gradio, imdb-dataset, machine-learning-algorithms, movie-recomendation-system, python3, standardscaler
- Language: Jupyter Notebook
- Homepage:
- Size: 61.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0