融合序列、图与特征工程，构建跨任务通用用户画像。
Blending Sequential Embeddings, Graphs, and Engineered Features: 4th Place Solution in RecSys Challenge 2025
- 用序列编码器捕捉用户兴趣随时间演变
- 图神经网络提升模型泛化能力
- 适合推荐系统竞赛与工业级建模场景
本文介绍团队ambitious在由Synerise和ACM RecSys联合举办的RecSys Challenge 2025中获得第四名的解决方案，该挑战聚焦于通用行为建模。目标是生成适用于六个不同下游任务的高效用户嵌入。我们的方法整合了（1）序列编码器以捕捉用户兴趣的时间演化，（2）图神经网络以增强模型泛化能力，（3）深度交叉网络以建模高阶特征交互，以及（4）性能关键的特征工程。
原文摘要 · Abstract (English)
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.
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