Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs). Owing to the exceptional generative capabilities of diffusion models, a few pioneering works explore developing GRs with diffusion architectures as the backbone. However, a fatal limitation of existing diffusion-based GRs is that the diffusion process applies uniformly to all items within the historical interactions. In contrast, the user preference is shaped by multifaceted time-evolving factors and thus exhibits a non-stationary distribution in the temporal aspect. To bridge this gap, this study proposes a novel GR framework, na...
| Authors | Bangguo Zhu, Peng Huo, Yuanbo Zhao et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 June 2026 |
| Last updated at source | 17 September 2026 |
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