Modeling Behavioral Intensity and Transitions for Generative Recommendation
Multi-behavior recommendation aims to predict user conversions by modeling various interaction types that carry distinct intent signals. Recently, generative sequence modeling methods have emerged as an important paradigm for multi-behavior recommendation by achieving flexible sequence generation. However, existing generative methods typically treat behaviors as auxiliary token features and feed them into unified attention mechanisms. These models implicitly assume uniform activation of dependencies among historical behaviors, thereby failing to discern differences in intensity or capture transition patterns. To address these limitations, we propose BITRec, a novel generative multi-behavi...
| Authors | Wenxuan Yang, Xiaoyang Xu, Hanyu Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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