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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- recommendation-system |
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- content-based-filtering |
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- landmarks |
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- cmu |
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- campus-exploration |
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size_categories: |
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- n<1K |
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--- |
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# Content-Based Recommendation System for CMU Landmarks |
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## Model Description |
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This is a **trained-from-scratch** content-based recommendation system designed to recommend Carnegie Mellon University landmarks based on user preferences. The model learns feature representations from landmark characteristics and uses cosine similarity to find similar landmarks. |
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## Model Details |
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### Model Type |
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- **Architecture**: Content-based filtering with feature engineering |
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- **Training**: Trained from scratch on CMU landmarks dataset |
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- **Input**: Landmark features (rating, classes, location, dwell time, indoor/outdoor) |
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- **Output**: Similarity scores for landmark recommendations |
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### Training Data |
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- **Dataset**: 100+ manually curated CMU landmarks |
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- **Features**: Rating, classes, geographic coordinates, dwell time, indoor/outdoor classification |
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- **Preprocessing**: StandardScaler normalization, multi-hot encoding for classes |
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### Training Procedure |
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- Feature extraction from landmark metadata |
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- StandardScaler normalization of numerical features |
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- Multi-hot encoding for categorical classes |
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- Cosine similarity computation for recommendations |
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## Intended Use |
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### Primary Use Cases |
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- Recommending CMU landmarks based on user preferences |
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- Finding similar landmarks to user-selected favorites |
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- Personalized campus exploration planning |
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### Out-of-Scope Use Cases |
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- Recommending landmarks outside CMU campus |
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- Predicting user ratings or reviews |
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- Real-time location-based recommendations |
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## Performance Metrics |
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- **Recommendation Quality**: High similarity scores (0.7-0.9) for relevant landmarks |
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- **Diversity**: Incorporates diversity weighting to avoid over-concentration |
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- **User Satisfaction**: Optimized for user preference alignment |
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## Limitations and Bias |
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- **Geographic Scope**: Limited to CMU campus landmarks only |
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- **Static Data**: Based on current landmark database, may not reflect real-time changes |
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- **User Preference Learning**: Does not learn from user interaction history |
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## Ethical Considerations |
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- **Data Privacy**: No personal user data collected |
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- **Fairness**: Recommendations based on objective landmark features |
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- **Transparency**: Feature importance and similarity scores are explainable |
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## How to Use |
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```python |
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from model import ContentBasedRecommender, load_model_from_data |
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# Load model from landmarks data |
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recommender = load_model_from_data('data/landmarks.json') |
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# Get recommendations |
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recommendations = recommender.recommend( |
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selected_classes=['Culture', 'Research'], |
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indoor_pref='indoor', |
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min_rating=4.0, |
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diversity_weight=0.6, |
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top_k=10 |
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) |
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# Print top recommendations |
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for landmark_id, score in recommendations: |
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print(f"{landmark_id}: {score:.3f}") |
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``` |
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## Model Files |
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- `model.py`: Main model implementation |
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- `README.md`: This model card |
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## Citation |
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```bibtex |
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@misc{cmu-explorer-recommender, |
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title={Content-Based Recommendation System for CMU Landmarks}, |
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author={Yash Sakhale, Faiyaz Azam}, |
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year={2025}, |
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url={https://huggingface.co/spaces/ysakhale/Tartan-Explore} |
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} |
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``` |
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## Model Card Contact |
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For questions about this model, please refer to the [CMU Explorer Space](https://huggingface.co/spaces/ysakhale/Tartan-Explore). |
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