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On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
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Proposes TA-Rec, a two-stage diffusion recommender that uses temporal consistency regularization in pretraining to enable one-step generation with bounded error, and adaptive preference alignment (DPO-style, step- and pair-similarity-weighted) in fine-tuning.
Problem
Multi-step diffusion-based sequential recommenders suffer from discretization error, creating a trade-off between inference efficiency (many denoising steps) and recommendation effectiveness when steps are reduced.
Approach
Smooth the denoising function via a temporal consistency loss between adjacent timesteps so that pure noise can be mapped to the target item in one step with O(Δt) error, then fine-tune with a preference-alignment loss whose strength adapts to timestep and positive-negative pair similarity.
Key results
- TA-Rec reports HR@20 of 6.15 on YooChoose, 5.40 on KuaiRec and 2.43 on Zhihu, the best among all baselines, with improvements of 7.14%, 4.65% and 7.52% over the strongest baseline in HR@20.
- Inference time on YooChoose drops to 00m06s for TA-Rec versus 23m14s for DreamRec (1,000 steps), with one-step generation.
- Ablations show removing TCR (w/o TCR) or APA (w/o APA) reduces performance, e.g., Zhihu HR@20 2.39 and 2.41 respectively versus 2.43 for the full model.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TA-Rec's two-stage TCR and APA framework instead of the strongest baseline | Sequential recommendation · YooChoose | HR@20: 5.74 -> 6.15 | Helped |
| Use TA-Rec's two-stage TCR and APA framework instead of the strongest baseline | Sequential recommendation · YooChoose | NDCG@20: 3.86 -> 4.06 | Helped |
| Use TA-Rec's two-stage TCR and APA framework instead of the strongest baseline | Sequential recommendation · KuaiRec | HR@20: 5.16 -> 5.40 | Helped |
| Use TA-Rec's two-stage TCR and APA framework instead of the strongest baseline | Sequential recommendation · KuaiRec | NDCG@20: 4.11 -> 4.42 | Helped |
| Use TA-Rec's two-stage TCR and APA framework instead of the strongest baseline | Sequential recommendation · Zhihu | HR@20: 2.26 -> 2.43 | Helped |
| Use TA-Rec's two-stage TCR and APA framework instead of the strongest baseline | Sequential recommendation · Zhihu | NDCG@20: 0.83 -> 0.91 | Helped |
| Add TCR during pretraining (ablation) | TA-Rec · YooChoose | HR@20: 5.72 -> 5.89 | Helped |
| Add APA fine-tuning (ablation) | TA-Rec · YooChoose | HR@20: 5.89 -> 6.15 | Helped |
| Use pair- and timestep-adaptive APA strength (ablation) | TA-Rec · YooChoose | HR@20: 6.10 -> 6.15 | Helped |
| Use pair- and timestep-adaptive APA strength (ablation) | TA-Rec · Zhihu | HR@20: 2.28 -> 2.43 | Helped |
| Add pair-similarity adaptation to APA (ablation) | TA-Rec · Zhihu | HR@20: 2.38 -> 2.43 | Helped |
| Add timestep adaptation to APA (ablation) | TA-Rec · Zhihu | HR@20: 2.35 -> 2.43 | Helped |
Surprising findings
- Hard-negative and popularity-based negative sampling give slightly better results than the random sampling used by default, suggesting the sampling strategy is not fully explored.
- The DDIM variant scores lower than the DDPM variant on YooChoose HR@20 (5.34 vs 5.68), so naive step reduction degrades performance.
Limitations
- Theoretical bound relies on assumed Lipschitz and smoothness conditions on the denoising function that are not verified empirically.
- Experiments are limited to three datasets with a fixed sequence length of 10 and reported standard deviations from five runs on a single RTX 3090 GPU.
Experimental setup
Datasets: YooChoose KuaiRec Zhihu
Benchmarks: YooChoose KuaiRec Zhihu
Metrics: HR@20 NDCG@20 coverage@20 running time
Baselines: GRU4Rec Caser SASRec Bert4Rec CL4SRec DiffRec DiffuRec DreamRec DiffuASR PDRec DimeRec PreferDiff
Builds on: DreamRec Diffusion-DPO DDPM DDIM PreferDiff
Closest papers by meaning
- Preference Diffusion for Recommendation (ICLR 2025 · similarity 0.90)
- Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation (NeurIPS 2025 · similarity 0.90)
- Enhancing Diffusion Model with Auxiliary Information Mining-Exploration and Efficient Sampling Mechanism for Sequential Recommendation (AAAI 2025 · similarity 0.88)
- Plug-In Diffusion Model for Sequential Recommendation (AAAI 2024 · similarity 0.87)
- Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommender Systems (NeurIPS 2026 · similarity 0.87)
- Learning Multiple User Distributions for Recommendation via Guided Conditional Diffusion (AAAI 2025 · similarity 0.86)
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