RecRec: Recursive Refinement for Sequential Recommendation
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. In this work, we revisit sequential recommendation from a recursive inference perspective: can user preferences be modeled as a persistent latent state that is recursively refined? We propose RecRec (Recursive Recommendation), a lightweight model that maintains a compact latent state and updates it through a shared recursive module conditioned on interaction evidence. Unlike prior recursive models, RecRec introduces an evidence-anchored correction mechanism that stabilizes...
| Authors | Pervez Shaik, Prosenjit Biswas, Abhinav Thorat et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 July 2026 |
| Last updated at source | 15 July 2026 |
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