Blending Sequential Embeddings, Graphs, and Engineered Features: 4th Place Solution in RecSys Challenge 2025
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912530360696832 |
|---|---|
| author | Makeev, Sergei Andreev, Alexandr Baikalov, Vladimir Tytskiy, Vladislav Krasilnikov, Aleksei Khrylchenko, Kirill |
| author_facet | Makeev, Sergei Andreev, Alexandr Baikalov, Vladimir Tytskiy, Vladislav Krasilnikov, Aleksei Khrylchenko, Kirill |
| contents | This paper describes the 4th-place solution by team ambitious for the RecSys Challenge 2025, organized by Synerise and ACM RecSys, which focused on universal behavioral modeling. The challenge objective was to generate user embeddings effective across six diverse downstream tasks. Our solution integrates (1) a sequential encoder to capture the temporal evolution of user interests, (2) a graph neural network to enhance generalization, (3) a deep cross network to model high-order feature interactions, and (4) performance-critical feature engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06970 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blending Sequential Embeddings, Graphs, and Engineered Features: 4th Place Solution in RecSys Challenge 2025 Makeev, Sergei Andreev, Alexandr Baikalov, Vladimir Tytskiy, Vladislav Krasilnikov, Aleksei Khrylchenko, Kirill Information Retrieval This paper describes the 4th-place solution by team ambitious for the RecSys Challenge 2025, organized by Synerise and ACM RecSys, which focused on universal behavioral modeling. The challenge objective was to generate user embeddings effective across six diverse downstream tasks. Our solution integrates (1) a sequential encoder to capture the temporal evolution of user interests, (2) a graph neural network to enhance generalization, (3) a deep cross network to model high-order feature interactions, and (4) performance-critical feature engineering. |
| title | Blending Sequential Embeddings, Graphs, and Engineered Features: 4th Place Solution in RecSys Challenge 2025 |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.06970 |