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| title: CineMind AI | |
| emoji: 🎬 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| sdk_version: 6.18.0 | |
| # CineMind AI Dataset Information | |
| ## Overview | |
| This project is built using the Netflix Prize dataset, a large-scale benchmark dataset widely used for recommendation system research. | |
| The original dataset contains: | |
| * 100,480,507 ratings | |
| * 480,189 anonymous users | |
| * 17,770 movie titles | |
| * Ratings collected between October 1998 and December 2005 | |
| * Rating scale: 1–5 stars | |
| To comply with licensing requirements and reduce deployment size, the full Netflix dataset is **not included** in this repository. | |
| Instead, this project uses processed and derived artifacts generated from the original dataset for recommendation inference and demonstration purposes. | |
| --- | |
| ## Project Purpose | |
| CineMind AI demonstrates the application of modern recommendation system techniques using: | |
| * DeepFM (Deep Factorization Machines) | |
| * NeuMF (Neural Matrix Factorization) | |
| * SVD++ | |
| The platform provides: | |
| * Personalized Movie Recommendations | |
| * Similar Movie Discovery | |
| * Intelligent Movie Search | |
| * Recommendation Analytics | |
| * Model Evaluation Dashboard | |
| --- | |
| ## Data Processing Pipeline | |
| Original Netflix Dataset | |
| ↓ | |
| Data Cleaning | |
| ↓ | |
| Feature Engineering | |
| ↓ | |
| Model Training | |
| ↓ | |
| Recommendation Generation | |
| ↓ | |
| Deployment Artifacts | |
| --- | |
| ## Models Evaluated | |
| ### DeepFM | |
| Combines: | |
| * Factorization Machines | |
| * Deep Neural Networks | |
| Captures both low-order and high-order feature interactions. | |
| ### NeuMF | |
| Neural collaborative filtering architecture for user-item interaction learning. | |
| ### SVD++ | |
| Matrix factorization approach incorporating implicit feedback. | |
| --- | |
| ## Evaluation Results | |
| | Metric | DeepFM | SVD++ | NeuMF | | |
| | ---------- | ------ | ----- | ----- | | |
| | RMSE | 0.969 | 0.981 | 0.986 | | |
| | MAE | 0.770 | 0.771 | 0.780 | | |
| | HitRate@10 | 0.457 | 0.318 | 0.312 | | |
| | NDCG@10 | 0.268 | 0.166 | 0.162 | | |
| | MAP@10 | 0.210 | 0.120 | 0.116 | | |
| DeepFM was selected as the final deployment model due to superior recommendation performance. | |
| --- | |
| ## Repository Contents | |
| This repository contains: | |
| * Trained DeepFM model | |
| * Encoders and preprocessing artifacts | |
| * Movie metadata | |
| * Application source code | |
| * Deployment configuration | |
| The original Netflix Prize dataset files are not redistributed. | |
| --- | |
| ## Dataset License Notice | |
| This project uses data derived from the Netflix Prize dataset. | |
| The original Netflix dataset remains subject to its respective usage restrictions and licensing terms. | |
| Users seeking access to the original dataset should refer to the official Netflix Prize documentation and licensing information. | |
| --- | |
| ## Author | |
| Hardik Gautam | |
| CineMind AI — DeepFM-Based Movie Recommendation System |