Update README.md

README.md

@@ -1,110 +1,110 @@
----
-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={CMU Explorer Team},
-  year={2024},
-  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).
+---
+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).