Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
cs.IR, cs.AI
Submitted: 2026-06-01
Updated: 2026-09-17
Comments: We wanna re-design the whole methodology and paper-writing
Code: https://github.com/Sylphy666/TDPM
License: http://creativecommons.org/licenses/by/4.0/
The gist: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs).
Terminology
Abstract
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, named TDPM, by designing the time-aware diffusion on SID tokens. Specifically, TDPM explicitly integrates the impact of time-evolving user preferences into the diffusion process. In detail, the user preference is disentangled into (i) the period preference, which remains consistent over a long time-span, and (ii) the point preference, which is triggered by recent focal events. Extensive experiments on three public real-world datasets demonstrate the significant superiority of TDPM over the state-of-the-art baselines. TDPM achieves average improvements of up to 29.21% and 25.45% in terms of HR@20 and NDCG@20, respectively. The ablation study further underscores the necessity of time-aware token diffusion in diffusion-based GRs.
Sources
- Session-based Recommendations with Recurrent Neural Networks
- Imagen Video: High Definition Video Generation with Diffusion Models
- Auto-Encoding Variational Bayes
- Decoupled Weight Decay Regularization
- Masked Diffusion for Generative Recommendation
- LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendation
- Denoising Diffusion Implicit Models
- LLaMA: Open and Efficient Foundation Language Models
- GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
- Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Related papers
- The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing
- SCAR: Semantic Continuity-Aware Retrieval for Efficient Context Expansion in RAG
- MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora
- RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
- Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval
- UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG