arXiv Machine Learning July 14, 2026 By Pervez Shaik, Prosenjit Biswas, Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar

RecRec: Recursive Refinement for Sequential Recommendation

Read the original on arXiv Machine Learning →

arXiv:2607. 10541v1 Announce Type: cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Generative Archetype-Grounded Item Representations for Sequential Recommendation

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.

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