AINeutralarXiv – CS AI · 6h ago6/10
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Bid Farewell to Seesaw: Towards Accurate Long-tail Session-based Recommendation via Dual Constraints of Hybrid Intents
Researchers propose HID, a machine learning framework that resolves the long-standing accuracy-versus-diversity trade-off in session-based recommendation systems by using hybrid intent learning and dual constraint losses. The approach identifies and filters session-irrelevant noise in long-tail items, enabling systems to boost both recommendation accuracy and diversity simultaneously rather than sacrificing one for the other.