You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This project implements a robust recommender system for book recommendations, leveraging ensemble methods, user-specific strategies, XGBoost, and extensive data preprocessing to achieve high performance in the Recommender System 2023 Challenge hosted by Kaggle for students of Politecnico di Milano's Recommender Systems course.
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
A case study of the Netflix Prize solution where, given anonymous data of users and the ratings given to movies, the objective to provide recommendations to users for movies which they would like, based on their past activity and taste.
This project is a recommendation system built with implicit ALS algorithm using Netflix UK's watch history data. It provides personalized movie recommendations and exposes a FastAPI API route for easy integration.