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In the practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that severely compromises recommendation diversity. Existing approaches attempt to address this issue by promoting tail items but incur accuracy degradation, exhibiting a \"see-saw\" effect between long-tail and accuracy performance. We attribute such conflict to session-irrelevant noise within the tail items, which existing long-tail approaches fail to identify and constrain effectively. To resolve this fundamental conflict, we propose \\textbf{HID} (\\textbf{H}ybrid \\textbf{I}ntent-based \\textbf{D}ual Constraint Framework), a plug-and-play framework that transforms the conventional \"see-saw\" into \"win-win\" through introducing the hybrid intent-based dual constraints for both l","title":"Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents","url":"https://arxiv.org/abs/2511.08378","vendor":"arxiv_cs_ai"},"summary":"arXiv:2511.08378v4 Announce Type: replace-cross \nAbstract: Session-based recommendation (SBR) aims to predict anonymous users' next interaction based on their interaction sessions. In the practical recommendation scenario, low-exposure items constitute the majority of interactions, creating a long-tail distribution that severely compromises recommendation diversity. Existing approaches attempt to address this issue by promoting tail items but incur accuracy degradation, exhibiting a \"see-saw\" effect between long-tail and accuracy performance. We attribute such conflict to session-irrelevant noise within the tail items, which existing long-tail approaches fail to identify and constrain effectively. 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