Explain and compare user-based and item-based collaborative filtering approaches
Compute user and item similarities using Pearson correlation and vector-based measures
Apply matrix factorization and SVD to predict ratings and handle missing values
Use stochastic gradient descent and bias terms to improve prediction accuracy
Model implicit feedback, demographics, and temporal dynamics in recommender systems
Apply social-network recommendation methods such as TidalTrust, TrustWalker, and random walks
