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Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
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Proposes UBTP (multi-step user behavior trajectory prediction) and LPDO, a diffusion training framework that injects a Plackett-Luce listwise ranking loss into the diffusion ELBO, evaluated with SeqMatch and PPL on Beauty, MovieLens-1M and LastFM.
Problem
Forecasting multi-step user behavior trajectories requires capturing listwise, global dependencies among future items, which next-item sequential recommenders and point-wise diffusion recommenders do not model.
Approach
Adds a Plackett-Luce / listwise ranking term to the diffusion ELBO over concatenated history and target-trajectory embeddings, using a causal transformer denoiser, so the denoising process is trained to rank the whole future list rather than each position independently.
Key results
- On MovieLens-1M (len=5), LPDO reaches SeqHR@5 0.1218 vs 0.1107 for the strongest baseline DCRec (+10.03%).
- On LastFM (len=5), LPDO improves SeqMatch@50 to 0.2357 from 0.1723 for DCRec (+36.81%).
- On Amazon Beauty (len=3), LPDO lowers PPL to 33.86 from 36.89 for DiffuRec (+8.21%).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use listwise preference loss instead of removing it (ablation) | LPDO · MovieLens-1M (len=5) | SeqHR@5: 0.0000 -> 0.1218 | Helped |
| use listwise preference loss instead of removing it (ablation) | LPDO · LastFM (len=5) | SeqHR@5: 0.0000 -> 0.2507 | Helped |
| include Simple reconstruction loss (ablation) | LPDO · MovieLens-1M (len=5) | SeqHR@5: 0.0563 -> 0.1218 | Helped |
| include Simple reconstruction loss (ablation) | LPDO · LastFM (len=5) | SeqHR@5: 0.1452 -> 0.2507 | Helped |
| include regularization loss (ablation) | LPDO · MovieLens-1M (len=5) | SeqHR@5: 0.0646 -> 0.1218 | Helped |
| include regularization loss (ablation) | LPDO · LastFM (len=5) | SeqHR@5: 0.2284 -> 0.2507 | Helped |
| replace bidirectional Transformer with causal Transformer (ablation) | LPDO · MovieLens-1M (len=5) | SeqHR@5: 0.1159 -> 0.1218 | Helped |
| replace prefix Transformer with causal Transformer (ablation) | LPDO · MovieLens-1M (len=5) | SeqHR@5: 0.1175 -> 0.1218 | Helped |
| increase inference denoising steps from 1 to 50 (ablation) | LPDO · MovieLens-1M (len=5) | SeqMatch@50: 0.1559 -> 0.1575 | Helped |
| increase inference denoising steps from 1 to 50 (ablation) | LPDO · MovieLens-1M (len=10) | SeqMatch@50: 0.0447 -> 0.0452 | Mixed |
| use LPDO instead of DCRec | user trajectory prediction · MovieLens-1M (len=5) | SeqMatch@50: 0.1458 -> 0.1559 | Helped |
| use LPDO instead of DCRec | user trajectory prediction · LastFM (len=5) | PPL: 29.28 -> 27.28 | Helped |
Surprising findings
- Diffusion baselines DreamRec and PreferDiff, trained without ranking loss, collapse to near-zero ranking metrics with PPL above 500 on several settings.
- Removing the ListPref loss drives all ranking metrics to zero, while removing the reconstruction loss still leaves nontrivial performance.
Limitations
- Evaluated only on three small sequential recommendation datasets with trajectory lengths of 3, 5, and 10.
- Baselines were adapted to multi-step prediction by the authors, so comparisons depend on those modifications.
- Reported significance claim is not backed by described test details.
Experimental setup
Datasets: Amazon Beauty MovieLens-1M LastFM
Benchmarks: Amazon Beauty MovieLens-1M LastFM
Metrics: SeqHR@K SeqNDCG@K SeqMatch@K PPL MeanHR@K MeanNDCG@K
Baselines: FPMC SASRec SASRec-AR STOSA DiffuRec DreamRec PreferDiff DCRec
Builds on: DCRec DDPM ListMLE BPR DiffuRec
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