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.

翻译：现有序列推荐模型依赖于特定数据集的训练，其学习参数适配于训练数据中的物品目录和观测到的交互分布。这限制了模型向新领域的泛化能力，通常需要从头开始重新训练。本文提出SRPFN——一种用于序列推荐的先验数据拟合网络：它可在目标域中通过单次前向传播预测下一物品，而无需任何基于梯度的参数更新。SRPFN在合成先验中采样的2560万条序列上进行离线预训练，该先验涵盖多样化的物品间转换模式，使模型学会生成后验预测的下一物品分布。在推理时，SRPFN通过条件化目标域中物品-物品转换示例的支持集生成推荐，无需重新训练即可适应领域特定模式。在10个基线方法上的5个基准测试中进行的广泛实验表明，SRPFN在几乎所有指标和数据集上达到最优或次优性能，同时计算效率显著高于训练型基线。这些结果证明：一个基于合成先验预训练的单一模型能够泛化至多样化的真实世界领域，为免更新序列推荐提供了框架。