Encode Me If You Can: Learning Universal User Representations via Event Sequence Autoencoding

TL;DR

This paper introduces a universal user representation learning method using a GRU autoencoder on event sequences, enabling effective behavior modeling for various predictive tasks.

Contribution

The paper presents a sequence autoencoding approach for learning task-independent user embeddings from raw interaction logs, improving generalization across multiple applications.

Findings

Achieved second place in RecSys Challenge 2025.

Ensemble of multiple embedding methods enhanced performance.

Autoencoder effectively captures key behavioral patterns.

Abstract

Building universal user representations that capture the essential aspects of user behavior is a crucial task for modern machine learning systems. In real-world applications, a user's historical interactions often serve as the foundation for solving a wide range of predictive tasks, such as churn prediction, recommendations, or lifetime value estimation. Using a task-independent user representation that is effective across all such tasks can reduce the need for task-specific feature engineering and model retraining, leading to more scalable and efficient machine learning pipelines. The goal of the RecSys Challenge 2025 by Synerise was to develop such Universal Behavioral Profiles from logs of past user behavior, which included various types of events such as product purchases, page views, and search queries. We propose a method that transforms the entire user interaction history into a single chronological sequence and trains a GRU-based autoencoder to reconstruct this sequence from a fixed-size vector. If the model can accurately reconstruct the sequence, the latent vector is expected to capture the key behavioral patterns. In addition to this core model, we explored several alternative methods for generating user embeddings and combined them by concatenating their output vectors into a unified representation. This ensemble strategy further improved generalization across diverse downstream tasks and helped our team, ai_lab_recsys, achieve second place in the RecSys Challenge 2025.