📢 How Recommendation Systems Work

🔍 What is a Recommendation System?

A recommendation system is an AI-powered technology that suggests content based on user behavior and data patterns.

Popular platforms like Netflix, Amazon, and Spotify use different types of recommendation systems to personalize user experiences.

📌 Collaborative Filtering (Netflix & Amazon)

This method finds users with similar preferences and recommends content based on what those similar users liked.

from surprise import SVD
from surprise import Dataset
from surprise.model_selection import cross_validate

data = Dataset.load_builtin('ml-100k')
algo = SVD()
cross_validate(algo, data, cv=5, verbose=True)
                

📌 Content-Based Filtering (Spotify & YouTube)

This method recommends items similar to what a user has already liked.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

movies = ["A sci-fi adventure in space",
          "A romantic comedy about love",
          "An action-packed superhero film",
          "A sci-fi story with aliens"]

vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(movies)
similarity = cosine_similarity(tfidf_matrix)

print(similarity)
                

📝 Quick Quiz

Which type of recommendation system does Netflix primarily use?

• Collaborative Filtering
• Content-Based Filtering
• Random Suggestions
• Hybrid Only