Recommender System

Machine learning algorithms: they predict user preferences (ratings, purchases) to combat information overload and drive targeted platform engagement.

A Recommender System (RS) is a critical AI application: it filters massive data sets to deliver personalized item suggestions, directly increasing user satisfaction and revenue (Amazon reports 35% of sales from recommendations). The core methods are Collaborative Filtering (CF): recommending items based on similar user behavior; and Content-Based Filtering (CBF): suggesting items matching a user’s past preferences (e.g., movie genres). Hybrid models combine both for high accuracy. Major platforms like Netflix, Spotify, and YouTube deploy these systems at scale, using techniques from matrix factorization to deep learning to maintain a competitive edge.

https://machinelearningmastering.com/how-to-get-started-with-recommender-systems/