Generative Sequential Recommendation via Hierarchical Behavior Modeling

• URL: http://arxiv.org/abs/2511.03155v1
• Date: Wed, 05 Nov 2025 03:27:01 GMT
• Title: Generative Sequential Recommendation via Hierarchical Behavior Modeling
• Authors: Zhefan Wang, Guokai Yan, Jinbei Yu, Siyu Gu, Jingyan Chen, Peng Jiang, Zhiqiang Guo, Min Zhang,
• Abstract summary: We propose a novel generative framework, GAMER, built upon a decoder-only backbone.<n> GAMER introduces a cross-level interaction layer to capture hierarchical dependencies among behaviors.<n>ShortVideoAD is a large-scale multi-behavior dataset from a mainstream short-video platform.
• Score: 20.156854767000475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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