Building a Recommender System using Machine Learning

Welcome to the first edition of a new article series called "The Kaggle Blueprints", where we will analyze Kaggle competitions' top solutions for lessons we can apply to our own data science projects. This first edition will review the techniques and approaches from the "OTTO -- Multi-Objective Recommender System" competition, which ended at the end of January, 2023. The goal of the "OTTO -- Multi-Objective Recommender System" competition was to build a multi-objective recommender system (RecSys) based on a large dataset of implicit user data. One of the main challenges of this competition was the large number of items to choose from. Feeding all of the available information into a complex model would require the availability of extensive amounts of computational resources.