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README.md

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+---
+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).

model.py

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+"""
+Content-Based Recommendation System for CMU Landmarks
+
+This model provides personalized landmark recommendations based on user preferences
+using content-based filtering with cosine similarity.
+"""
+
+import numpy as np
+from typing import List, Dict, Tuple, Optional
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+import json
+import pickle
+
+
+class ContentBasedRecommender:
+    """
+    Content-Based Recommendation System (Trained-from-scratch)
+    
+    Uses landmark features to recommend similar landmarks based on user preferences.
+    This is a trained-from-scratch model that learns from the landmark dataset.
+    """
+    
+    def __init__(self, landmarks_data: List[Dict] = None):
+        self.landmarks = landmarks_data or []
+        self.feature_matrix = None
+        self.scaler = StandardScaler()
+        self.class_encoder = LabelEncoder()
+        self.landmark_ids = []
+        
+        if landmarks_data:
+            self._build_feature_matrix()
+    
+    def _build_feature_matrix(self):
+        """Build feature matrix from landmark data"""
+        features = []
+        all_classes = []
+        
+        # Collect all unique classes for encoding
+        for lm in self.landmarks:
+            all_classes.extend(lm.get('Class', []))
+        
+        unique_classes = sorted(list(set(all_classes)))
+        if unique_classes:
+            self.class_encoder.fit(unique_classes)
+        
+        # Create feature vectors for each landmark
+        for lm in self.landmarks:
+            feature_vector = self._extract_features(lm, unique_classes)
+            features.append(feature_vector)
+            self.landmark_ids.append(lm['id'])
+        
+        # Convert to numpy array and scale
+        if features:
+            self.feature_matrix = np.array(features)
+            self.feature_matrix = self.scaler.fit_transform(self.feature_matrix)
+    
+    def _extract_features(self, landmark: Dict, all_classes: List[str]) -> np.ndarray:
+        """Extract numerical features from a landmark"""
+        features = []
+        
+        # Rating (normalized to 0-1)
+        rating = landmark.get('rating', 0.0)
+        features.append(rating / 5.0)
+        
+        # Indoor/outdoor (binary encoding)
+        io_type = landmark.get('indoor/outdoor', 'outdoor')
+        features.append(1.0 if io_type == 'indoor' else 0.0)
+        
+        # Dwell time (normalized)
+        dwell_min = landmark.get('time taken to explore', 30)
+        features.append(dwell_min / 480.0)
+        
+        # Class encoding (multi-hot encoding)
+        class_vector = np.zeros(len(all_classes))
+        landmark_classes = landmark.get('Class', [])
+        for cls in landmark_classes:
+            if cls in all_classes:
+                idx = all_classes.index(cls)
+                class_vector[idx] = 1.0
+        features.extend(class_vector)
+        
+        # Geographic features (normalized lat/lon around CMU)
+        cmu_lat, cmu_lon = 40.4433, -79.9436
+        geocoord = landmark.get('geocoord', {'lat': cmu_lat, 'lon': cmu_lon})
+        features.append(abs(geocoord['lat'] - cmu_lat) / 0.1)
+        features.append(abs(geocoord['lon'] - cmu_lon) / 0.1)
+        
+        return np.array(features)
+    
+    def get_user_preference_vector(self, selected_classes: List[str], 
+                                 indoor_pref: Optional[str] = None,
+                                 min_rating: float = 0.0) -> np.ndarray:
+        """Create user preference vector from selections"""
+        if not self.feature_matrix.size:
+            return np.array([])
+            
+        all_classes = self.class_encoder.classes_
+        
+        # Start with average landmark profile
+        user_vector = np.mean(self.feature_matrix, axis=0)
+        
+        # Boost selected classes
+        if selected_classes:
+            class_mask = np.zeros(len(all_classes))
+            for cls in selected_classes:
+                if cls in all_classes:
+                    idx = list(all_classes).index(cls)
+                    class_mask[idx] = 1.0
+            
+            # Add class preferences to user vector
+            class_start_idx = 3  # After rating, indoor/outdoor, dwell_time
+            class_end_idx = class_start_idx + len(all_classes)
+            user_vector[class_start_idx:class_end_idx] += class_mask * 0.5
+        
+        # Indoor/outdoor preference
+        if indoor_pref == 'indoor':
+            user_vector[1] += 0.3
+        elif indoor_pref == 'outdoor':
+            user_vector[1] -= 0.3
+        
+        return user_vector
+    
+    def recommend(self, selected_classes: List[str], 
+                 indoor_pref: Optional[str] = None,
+                 min_rating: float = 0.0,
+                 diversity_weight: float = 0.6,
+                 exclude_ids: List[str] = None,
+                 top_k: int = 10) -> List[Tuple[str, float]]:
+        """
+        Get recommendations based on user preferences
+        
+        Returns list of (landmark_id, similarity_score) tuples
+        """
+        if not self.feature_matrix.size:
+            return []
+            
+        if exclude_ids is None:
+            exclude_ids = []
+        
+        # Get user preference vector
+        user_vector = self.get_user_preference_vector(selected_classes, indoor_pref, min_rating)
+        
+        # Calculate similarities
+        similarities = cosine_similarity([user_vector], self.feature_matrix)[0]
+        
+        # Filter by minimum rating and excluded IDs
+        filtered_results = []
+        for i, lm in enumerate(self.landmarks):
+            if (lm.get('rating', 0) >= min_rating and 
+                lm['id'] not in exclude_ids and
+                i < len(similarities)):
+                
+                # Apply diversity weighting
+                base_score = similarities[i]
+                
+                # Diversity bonus based on class rarity
+                class_diversity = self._calculate_diversity_bonus(lm, selected_classes)
+                final_score = base_score + diversity_weight * class_diversity
+                
+                filtered_results.append((lm['id'], final_score))
+        
+        # Sort by score (descending) and return top_k
+        sorted_results = sorted(filtered_results, key=lambda x: x[1], reverse=True)
+        return sorted_results[:top_k]
+    
+    def _calculate_diversity_bonus(self, landmark: Dict, selected_classes: List[str]) -> float:
+        """Calculate diversity bonus for a landmark"""
+        landmark_classes = set(landmark.get('Class', []))
+        selected_classes_set = set(selected_classes)
+        new_classes = landmark_classes - selected_classes_set
+        return len(new_classes) * 0.1  # Small bonus for diversity
+    
+    def save_model(self, filepath: str):
+        """Save the trained model"""
+        model_data = {
+            'feature_matrix': self.feature_matrix.tolist() if self.feature_matrix is not None else None,
+            'landmark_ids': self.landmark_ids,
+            'scaler_mean': self.scaler.mean_.tolist() if hasattr(self.scaler, 'mean_') else None,
+            'scaler_scale': self.scaler.scale_.tolist() if hasattr(self.scaler, 'scale_') else None,
+            'class_encoder_classes': self.class_encoder.classes_.tolist() if hasattr(self.class_encoder, 'classes_') else None
+        }
+        
+        with open(filepath, 'w') as f:
+            json.dump(model_data, f)
+    
+    def load_model(self, filepath: str):
+        """Load a trained model"""
+        with open(filepath, 'r') as f:
+            model_data = json.load(f)
+        
+        self.feature_matrix = np.array(model_data['feature_matrix']) if model_data['feature_matrix'] else None
+        self.landmark_ids = model_data['landmark_ids']
+        
+        if model_data['scaler_mean']:
+            self.scaler.mean_ = np.array(model_data['scaler_mean'])
+            self.scaler.scale_ = np.array(model_data['scaler_scale'])
+        
+        if model_data['class_encoder_classes']:
+            self.class_encoder.classes_ = np.array(model_data['class_encoder_classes'])
+
+
+def load_model_from_data(data_path: str) -> ContentBasedRecommender:
+    """Load model from landmarks data"""
+    with open(data_path, 'r') as f:
+        landmarks = json.load(f)
+    
+    recommender = ContentBasedRecommender(landmarks)
+    return recommender
+
+
+# Example usage
+if __name__ == "__main__":
+    # Load landmarks data
+    with open('data/landmarks.json', 'r') as f:
+        landmarks = json.load(f)
+    
+    # Initialize recommender
+    recommender = ContentBasedRecommender(landmarks)
+    
+    # Get recommendations
+    recommendations = recommender.recommend(
+        selected_classes=['Culture', 'Research'],
+        indoor_pref='indoor',
+        min_rating=4.0,
+        top_k=5
+    )
+    
+    print("Top 5 recommendations:")
+    for lm_id, score in recommendations:
+        print(f"{lm_id}: {score:.3f}")