Build a Recommendation Algorithm Using PySpark

Task: Implement a recommendation algorithm using PySpark. Approach: Load and preprocess interaction data at scale using Spark DataFrames, then apply a distributed collaborative-filtering approach (e.g. Spark MLlib's ALS - Alternating Least Squares) to factorize the user-item interaction matrix, or a content/feature-based scoring pipeline if collaborative signal is sparse. Discuss partitioning/scaling considerations and how to serve/refresh recommendations in a production PySpark pipeline.