/recommendation-engine

name: recommendation-engine
description: Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
license: MIT
metadata:
  keywords: "recommendation engine, collaborative filtering, matrix factorization, SVD, ALS, NMF, user-based CF, item-based CF, cold start, precision@k, recall@k, NDCG, hybrid recommender, content-based filtering, cosine similarity, implicit feedback, evaluation metrics, diversity, coverage"

Recommendation Engine

Build recommendation systems for personalized content and product suggestions.

Recommendation Approaches

| Approach | How It Works | Pros | Cons | |----------|--------------|------|------| | Collaborative | User-item interactions | Discovers hidden patterns | Cold start | | Content-based | Item features | Works for new items | Limited discovery | | Hybrid | Combines both | Best of both | Complex |

Collaborative Filtering

import numpy as np
from scipy.sparse import csr_matrix
from sklearn.metrics.pairwise import cosine_similarity

class CollaborativeFilter:
    def __init__(self):
        self.user_similarity = None
        self.item_similarity = None

    def fit(self, user_item_matrix):
        # User-based similarity
        self.user_similarity = cosine_similarity(user_item_matrix)
        # Item-based similarity
        self.item_similarity = cosine_similarity(user_item_matrix.T)

    def recommend_for_user(self, user_id, n=10):
        scores = self.user_similarity[user_id].dot(self.user_item_matrix)
        # Exclude already interacted items
        already_interacted = self.user_item_matrix[user_id].nonzero()[0]
        scores[already_interacted] = -np.inf
        return np.argsort(scores)[-n:][::-1]

Matrix Factorization (SVD)

from sklearn.decomposition import TruncatedSVD

class MatrixFactorization:
    def __init__(self, n_factors=50):
        self.svd = TruncatedSVD(n_components=n_factors)

    def fit(self, user_item_matrix):
        self.user_factors = self.svd.fit_transform(user_item_matrix)
        self.item_factors = self.svd.components_.T

    def predict(self, user_id, item_id):
        return np.dot(self.user_factors[user_id], self.item_factors[item_id])

Hybrid Recommender

class HybridRecommender:
    def __init__(self, collab_weight=0.7, content_weight=0.3):
        self.collab = CollaborativeFilter()
        self.content = ContentBasedFilter()
        self.weights = (collab_weight, content_weight)

    def recommend(self, user_id, n=10):
        collab_scores = self.collab.score(user_id)
        content_scores = self.content.score(user_id)
        combined = self.weights[0] * collab_scores + self.weights[1] * content_scores
        return np.argsort(combined)[-n:][::-1]

Evaluation Metrics

• Precision@K, Recall@K
• NDCG (ranking quality)
• Coverage (catalog diversity)
• A/B test conversion rate

Cold Start Solutions

• **New users**: Popular items, onboarding preferences, demographic-based
• **New items**: Content-based bootstrapping, active learning
• **Exploration strategies**: ε-greedy, Thompson sampling bandits

Quick Start: Build a Recommender in 5 Steps

from scipy.sparse import csr_matrix
import numpy as np

# 1. Prepare user-item interaction matrix
# rows = users, cols = items, values = ratings/interactions
ratings_data = [(0, 5, 5), (0, 10, 4), (1, 5, 3), ...]  # (user, item, rating)
n_users, n_items = 1000, 5000

row_idx = [r[0] for r in ratings_data]
col_idx = [r[1] for r in ratings_data]
ratings = [r[2] for r in ratings_data]
user_item_matrix = csr_matrix((ratings, (row_idx, col_idx)), shape=(n_users, n_items))

# 2. Choose and train model
from recommendation_engine import ItemBasedCollaborativeFilter  # See references

model = ItemBasedCollaborativeFilter(similarity_metric='cosine', k_neighbors=20)
model.fit(user_item_matrix)

# 3. Generate recommendations
recommendations = model.recommend(user_id=42, n=10)
print(recommendations)  # [(item_id, score), ...]

# 4. Evaluate on test set
from evaluation_metrics import precision_at_k, recall_at_k

test_items = {42: {10, 25, 30}}  # True relevant items for user 42
rec_items = [item for item, score in recommendations]

precision = precision_at_k(rec_items, test_items[42], k=10)
recall = recall_at_k(rec_items, test_items[42], k=10)
print(f"Precision@10: {precision:.3f}, Recall@10: {recall:.3f}")

# 5. Handle cold start
from cold_start import PopularityRecommender

popularity_model = PopularityRecommender()
popularity_model.fit(interactions_with_timestamps)
new_user_recs = popularity_model.recommend(n=10)

Known Issues Prevention

1. Popularity Bias

**Problem**: Recommending only popular items, ignoring long tail. Reduces diversity and serendipity.

**Solution**: Balance popularity with personalization, apply re-ranking for diversity:

def diversify_recommendations(
    recommendations: List[Tuple[int, float]],
    item_features: np.ndarray,
    diversity_weight: float = 0.3
) -> List[Tuple[int, float]]:
    """Re-rank to increase diversity while maintaining relevance."""
    from sklearn.metrics.pairwise import cosine_distances

    selected = []
    candidates = recommendations.copy()

    while len(selected) < len(recommendations) and candidates:
        if not selected:
            # First item: highest score
            selected.append(candidates.pop(0))
            continue

        # Compute diversity scores
        selected_features = item_features[[item for item, _ in selected]]
        diversity_scores = []

        for item, relevance in candidates:
            item_feature = item_features[item].reshape(1, -1)
            # Average distance to already selected items
            avg_distance = cosine_distances(item_feature, selected_features).mean()
            # Combined score: relevance + diversity