Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

This paper introduces new flexible loss functions for binary classification in Gradient-
Boosted Decision Trees (GBDT) that combine Dice-based and cross-entropy-based losses …

Session-based recommendation is an important task for domains like e-commerce, that
suffer from the user cold-start problem due to anonymous browsing and for which users …

Recommender systems developers are constantly faced with difficult design decisions.
Additionally, the number of options that a recommender systems developer has to consider …

In this paper, we present our 5th place solution for the ACM RecSys 2022 challenge
(http://www. recsyschallenge. com/2022/). The competition, organized by Dressipi, aims to …

This article investigates the information landscape shaped by curation algorithms that seek
to maximize user engagement. Leveraging unique behavioral data, we trained machine …

Recommender systems are an essential component of online platforms providing users with
personalized experiences. Some recommendation scenarios such as social networks and …

Text embedding models have been popular for information retrieval applications such as
semantic search and Question-Answering systems based on Retrieval-Augmented …

Industrial recommender systems are made up of complex pipelines requiring multiple steps
including feature engineering and preprocessing, a retrieval model for candidate …

In this paper we provide an overview of the approach we used as team Surricchi1 for the
ACM RecSys Challenge 20221. The competition, sponsored and organized by Dressipi …