RIDRec: Retrieval-Enhanced Intent Diffusion for Anonymous Short-Session Recommendation

Abstract

Anonymous short-session recommendation aims to predict the next item from only a few interactions in the current session, without relying on persistent user identities or long-term preference profiles. This setting is particularly challenging because the observed behavioral evidence is often sparse, multiple latent intents may coexist within the same short session, and useful cross-session historical signals are not always effectively exploited. As a result, models that rely mainly on a deterministic session representation often struggle to distinguish between several equally plausible intent explanations under limited observations. To address these issues, we propose RIDRec, a retrieval-enhanced probabilistic framework for anonymous short-session recommendation. Specifically, RIDRec first retrieves relevant cross-session contexts to construct retrieval-conditioned context features and prior-conditioning signals that complement sparse in-session evidence. It then models session intent as a latent distribution with Neural Processes, allowing the model to preserve intent uncertainty and capture multiple plausible interests without predefining the number of intents. On top of this, RIDRec further introduces a conditional diffusion module to progressively refine latent interest representations, making them more robust and discriminative for final ranking. We conduct experiments on multiple public benchmarks under a unified full-ranking evaluation protocol. The results show that RIDRec consistently outperforms strong baselines in both ranking accuracy and item coverage. Moreover, under a 20% training-data setting, RIDRec remains competitive and still surpasses the strongest full-data baseline on RetailRocket in terms of HR@10. Overall, RIDRec provides an effective unified framework for anonymous short-session recommendation. Code is available at RIDRec.