recommendation-engine

# 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 ```python 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) ```python from sklearn.decomposition import TruncatedSVD class MatrixFactorization: def __init__(self, n_factors=50): self.svd = TruncatedSVD(n_components=n_factors) def fit(...