# RecSys Challenge 2023: From data preparation to prediction, a simple, efficient, robust and scalable solution

arXiv: https://arxiv.org/abs/2401.06830 · HTML: https://mlsanity.com/paper/2401.06830/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [An Overview of General Performance Metrics of Binary Classifier Systems](https://mlsanity.com/paper/1410.5330/) (2014)
- [Exploring Online Ad Images Using a Deep Convolutional Neural Network Approach](https://mlsanity.com/paper/1509.00568/) (2015)

## Abstract (arXiv)

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.