Beyond Aggregation: A Feature-Fused Universal Behavioral Transformer for Generalizable User Representation

Modern enterprises require robust predictive analytics, yet tasks like churn prediction and propensity modeling are often treated as isolated problems. The ACM RecSys Challenge 2025 addressed this by proposing a unified task to create generalizable user profiles from a large-scale behavioral dataset provided by Synerise. This paper presents the 4th-place solution in the academic track from team "ririka". Our approach centers on creating a powerful, hybrid Universal Behavioral Profile by fusing an extensive, feature-engineered vector with multiple specialized embeddings from a Universal Behavioral Transformer (UBT). We introduce a comprehensive feature engineering strategy incorporating statistical, temporal, and semantic features, and develop a multi-task UBT framework to generate distinct embeddings for objectives like churn and propensity. Through a meticulous fusion and normalization methodology, we combine these components into a single, high-dimensional user representation. Our final model proved highly effective, demonstrating robust and generalizable performance across both open and hidden evaluation tasks. Our analysis confirms that synthesizing handcrafted features with deep, sequential representations is critical for building a powerful and versatile user profile.