---
license: mit
language:
- en
tags:
- recommendation-system
- content-based-filtering
- landmarks
- cmu
- campus-exploration
size_categories:
- n<1K
---

# Content-Based Recommendation System for CMU Landmarks

## Model Description

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.

## Model Details

### Model Type
- **Architecture**: Content-based filtering with feature engineering
- **Training**: Trained from scratch on CMU landmarks dataset
- **Input**: Landmark features (rating, classes, location, dwell time, indoor/outdoor)
- **Output**: Similarity scores for landmark recommendations

### Training Data
- **Dataset**: 100+ manually curated CMU landmarks
- **Features**: Rating, classes, geographic coordinates, dwell time, indoor/outdoor classification
- **Preprocessing**: StandardScaler normalization, multi-hot encoding for classes

### Training Procedure
- Feature extraction from landmark metadata
- StandardScaler normalization of numerical features
- Multi-hot encoding for categorical classes
- Cosine similarity computation for recommendations

## Intended Use

### Primary Use Cases
- Recommending CMU landmarks based on user preferences
- Finding similar landmarks to user-selected favorites
- Personalized campus exploration planning

### Out-of-Scope Use Cases
- Recommending landmarks outside CMU campus
- Predicting user ratings or reviews
- Real-time location-based recommendations

## Performance Metrics

- **Recommendation Quality**: High similarity scores (0.7-0.9) for relevant landmarks
- **Diversity**: Incorporates diversity weighting to avoid over-concentration
- **User Satisfaction**: Optimized for user preference alignment

## Limitations and Bias

- **Geographic Scope**: Limited to CMU campus landmarks only
- **Static Data**: Based on current landmark database, may not reflect real-time changes
- **User Preference Learning**: Does not learn from user interaction history

## Ethical Considerations

- **Data Privacy**: No personal user data collected
- **Fairness**: Recommendations based on objective landmark features
- **Transparency**: Feature importance and similarity scores are explainable

## How to Use

```python
from model import ContentBasedRecommender, load_model_from_data

# Load model from landmarks data
recommender = load_model_from_data('data/landmarks.json')

# Get recommendations
recommendations = recommender.recommend(
    selected_classes=['Culture', 'Research'],
    indoor_pref='indoor',
    min_rating=4.0,
    diversity_weight=0.6,
    top_k=10
)

# Print top recommendations
for landmark_id, score in recommendations:
    print(f"{landmark_id}: {score:.3f}")
```

## Model Files

- `model.py`: Main model implementation
- `README.md`: This model card

## Citation

```bibtex
@misc{cmu-explorer-recommender,
  title={Content-Based Recommendation System for CMU Landmarks},
  author={Yash Sakhale, Faiyaz Azam},
  year={2025},
  url={https://huggingface.co/spaces/ysakhale/Tartan-Explore}
}
```

## Model Card Contact

For questions about this model, please refer to the [CMU Explorer Space](https://huggingface.co/spaces/ysakhale/Tartan-Explore).