Exploiting Contextual Normalizations and Article Endorsement for News Recommendation

Andrea Alari, Lorenzo Campana, Federico Giuseppe Ciliberto, Saverio Maggese, Carlo Sgaravatti, Francesco Zanella, Andrea Pisani, Maurizio Ferrari Dacrema

Abstract: We provide an overview of the approach used as team FeatureSalad for the ACM RecSys Challenge 2024, organized by Ekstra Bladet. The competition addressed the problem of News Recommendation, where the goal is to predict which article a user will click on given the list of articles that are shown to them. Our solution is based on a stacking ensemble of consolidated algorithms, such as gradient boosting for decision trees and neural networks. It relies on numerous features, which model the interest of a user and the lifecycle of an article. The proposed solution allowed our team to rank first among the academic teams, and sixth overall.