Generative Sequential Recommendation via Hierarchical Behavior Modeling

TL;DR

This paper introduces GAMER, a generative framework with hierarchical behavior modeling for multi-behavior recommendation, and releases a new dataset to improve validation beyond e-commerce domains.

Contribution

The paper proposes GAMER, a novel generative model with cross-level interaction and augmentation, and provides a new large-scale multi-behavior dataset from a short-video platform.

Findings

GAMER outperforms baselines across multiple metrics.

The hierarchical modeling captures complex user behavior dependencies.

The new dataset enables validation in non-e-commerce domains.

Abstract

Recommender systems in multi-behavior domains, such as advertising and e-commerce, aim to guide users toward high-value but inherently sparse conversions. Leveraging auxiliary behaviors (e.g., clicks, likes, shares) is therefore essential. Recent progress on generative recommendations has brought new possibilities for multi-behavior sequential recommendation. However, existing generative approaches face two significant challenges: 1) Inadequate Sequence Modeling: capture the complex, cross-level dependencies within user behavior sequences, and 2) Lack of Suitable Datasets: publicly available multi-behavior recommendation datasets are almost exclusively derived from e-commerce platforms, limiting the validation of feasibility in other domains, while also lacking sufficient side information for semantic ID generation. To address these issues, we propose a novel generative framework, GAMER (Generative Augmentation and Multi-lEvel behavior modeling for Recommendation), built upon a decoder-only backbone. GAMER introduces a cross-level interaction layer to capture hierarchical dependencies among behaviors and a sequential augmentation strategy that enhances robustness in training. To further advance this direction, we collect and release ShortVideoAD, a large-scale multi-behavior dataset from a mainstream short-video platform, which differs fundamentally from existing e-commerce datasets and provides pretrained semantic IDs for research on generative methods. Extensive experiments show that GAMER consistently outperforms both discriminative and generative baselines across multiple metrics.