一个预训练模型无需微调即可跨数据集推荐，效率更高。
One Sequential Recommendation Model Pretrained from Synthetic Priors Predicts Multiple Datasets
- 用合成数据预训练模型，推理时仅需输入目标域样本即刻适配
- 在5个数据集上表现最佳或第二，计算开销远低于传统方法
- 适合需要快速部署、避免重训的推荐系统场景
现有序列推荐模型依赖特定数据集训练，参数仅适应训练数据的物品目录与交互分布，限制了跨领域泛化能力，通常需从头重新训练。本文提出SRPFN——一种面向序列推荐的先验拟合网络，可在单次前向传播中预测下一个物品，无需在目标域进行梯度更新。SRPFN在2560万条从涵盖多样物品转移模式的合成先验中采样的序列上离线预训练，学习生成后验预测的下一个物品分布。推理时，通过条件化于目标域的物品-物品转移示例支持集，实现对领域特有模式的适应，无需重训。在5个基准数据集上，对比10种基线方法，SRPFN在几乎所有指标和数据集上达到最佳或第二佳表现，且计算效率显著优于训练型模型。结果表明，单一在合成先验上预训练的模型可跨多样真实世界领域泛化，为无更新序列推荐提供新框架。
原文摘要 · Abstract (English)
Existing sequential recommendation models rely on dataset-specific training, where the learned parameters are fitted to the item catalog and the observed interaction distribution of the training data. This limits generalization to new domains, typically requiring retraining from scratch. In this work, we propose SRPFN, a Prior-data Fitted Network for sequential recommendation -- predicting the next item in a single forward pass without any gradient-based parameter updates in the target domain. SRPFN is pretrained offline on 25.6M sequences sampled from a synthetic prior that spans diverse item-to-item transition patterns, learning to produce posterior predictive next-item distributions. At inference time, SRPFN generates recommendations by conditioning on a support set of item-item transition examples from the target domain, adapting to domain-specific patterns without retraining. Extensive experiments on five benchmarks across 10 baselines show that SRPFN achieves the best or second-best performance across nearly all metrics and datasets, while being substantially more computationally efficient than trained baselines. These results establish that a single model pretrained on synthetic priors can generalize across diverse real-world domains, offering a framework for update-free sequential recommendation.
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