AI Applications

# Example: Simple Content Recommendation System
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
class RecommendationEngine:
    """
    Collaborative filtering recommendation system
    Used by Netflix, Amazon, Spotify, etc.
    """
    def __init__(self):
        # User-item interaction matrix
        # Rows: users, Columns: items, Values: ratings
        self.user_item_matrix = None
        self.user_similarity = None
    def fit(self, user_item_matrix):
        """Train the recommendation model"""
        self.user_item_matrix = user_item_matrix
        # Compute user similarity using cosine similarity
        # Users with similar preferences get high similarity scores
        self.user_similarity = cosine_similarity(user_item_matrix)
    def recommend(self, user_id, n_recommendations=5):
        """Generate top N recommendations for a user"""
        # Find users similar to target user
        similar_users = self.user_similarity[user_id]
        # Weight items by similarity to get scores
        # Items liked by similar users get higher scores
        weighted_scores = np.dot(similar_users, self.user_item_matrix)
        # Get items user hasn't interacted with
        user_items = self.user_item_matrix[user_id]
        weighted_scores = weighted_scores * (user_items == 0)
        # Return top N items
        top_items = np.argsort(weighted_scores)[-n_recommendations:][::-1]
        return top_items
# Example usage
# User-item matrix: 5 users, 6 items
# 1 = watched/liked, 0 = not watched
user_item_matrix = np.array([
    [1, 1, 0, 0, 1, 0],  # User 0
    [1, 0, 1, 0, 0, 1],  # User 1
    [0, 1, 1, 1, 0, 0],  # User 2
    [0, 0, 1, 1, 1, 1],  # User 3
    [1, 1, 0, 0, 0, 0],  # User 4 (similar to User 0)
])
engine = RecommendationEngine()
engine.fit(user_item_matrix)
# Recommend items for User 0
recommendations = engine.recommend(user_id=0, n_recommendations=3)
print(f"Recommended items for User 0: {recommendations}")
# This powers 35% of Amazon's revenue and 80% of Netflix views!