Saved in:
Bibliographic Details
Main Authors: Manderlier, Maxime, Lecron, Fabian
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2401.06830
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911756813598720
author Manderlier, Maxime
Lecron, Fabian
author_facet Manderlier, Maxime
Lecron, Fabian
contents The RecSys Challenge 2023, presented by ShareChat, consists to predict if an user will install an application on his smartphone after having seen advertising impressions in ShareChat & Moj apps. This paper presents the solution of 'Team UMONS' to this challenge, giving accurate results (our best score is 6.622686) with a relatively small model that can be easily implemented in different production configurations. Our solution scales well when increasing the dataset size and can be used with datasets containing missing values.
format Preprint
id arxiv_https___arxiv_org_abs_2401_06830
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle RecSys Challenge 2023: From data preparation to prediction, a simple, efficient, robust and scalable solution
Manderlier, Maxime
Lecron, Fabian
Information Retrieval
Artificial Intelligence
Machine Learning
The RecSys Challenge 2023, presented by ShareChat, consists to predict if an user will install an application on his smartphone after having seen advertising impressions in ShareChat & Moj apps. This paper presents the solution of 'Team UMONS' to this challenge, giving accurate results (our best score is 6.622686) with a relatively small model that can be easily implemented in different production configurations. Our solution scales well when increasing the dataset size and can be used with datasets containing missing values.
title RecSys Challenge 2023: From data preparation to prediction, a simple, efficient, robust and scalable solution
topic Information Retrieval
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2401.06830