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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2401.06830 |
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| _version_ | 1866911756813598720 |
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| 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 |