RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations

Abstract:We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation. RecPFN is pretrained entirely on synthetic clickstream environments sampled from a broad structural causal prior, enabling it to amortize Bayesian-style inference from a small support set. At inference, a lightweight decoder-only transformer conditions on a handful of domain sequences and produces next-item predictions for queries in a single forward pass, without any weight updates. Across eight public benchmarks, RecPFN achives state-of-the-art zero-shot performance while remaining strongly competitive with supervised methods in low-compute and low-data regimes. It is deployment-efficient and robust to domain shift, outperforming strong zero-shot baselines that rely on large real-interaction corpora. RecPFN provides a practical path toward generalizable, data-efficient recommenders and opens avenues for richer priors, longer-context ICL, and multimodal extensions. Code for training and evaluation is publicly available at this https URL.

Machine Learning (cs.LG)

arXiv:2608.19735 [cs.LG]

(or arXiv:2608.19735v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.19735 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

In Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1731-1742. 2026

https://doi.org/10.1145/3805712.3809696 Focus to learn more DOI(s) linking to related resources

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