Generative Chain of Behavior for User Trajectory Prediction

TL;DR

This paper introduces GCB, a generative framework that models long-term user behavior trajectories as an autoregressive chain, improving multi-step prediction accuracy and capturing evolving preferences.

Contribution

GCB is a novel generative model that encodes items into a semantic latent space and uses a transformer to predict multi-step user behaviors, addressing limitations of existing sequential recommenders.

Findings

Outperforms state-of-the-art in multi-step accuracy

Generates coherent long-term user trajectories

Effectively captures evolving user preferences

Abstract

Modeling long-term user behavior trajectories is essential for understanding evolving preferences and enabling proactive recommendations. However, most sequential recommenders focus on next-item prediction, overlooking dependencies across multiple future actions. We propose Generative Chain of Behavior (GCB), a generative framework that models user interactions as an autoregressive chain of semantic behaviors over multiple future steps. GCB first encodes items into semantic IDs via RQ-VAE with k-means refinement, forming a discrete latent space that preserves semantic proximity. On top of this space, a transformer-based autoregressive generator predicts multi-step future behaviors conditioned on user history, capturing long-horizon intent transitions and generating coherent trajectories. Experiments on benchmark datasets show that GCB consistently outperforms state-of-the-art sequential recommenders in multi-step accuracy and trajectory consistency. Beyond these gains, GCB offers a unified generative formulation for capturing user preference evolution.