LumiCRS shows that combining a tailored focal loss, prototype-guided representation learning, and LLM-generated tail dialogue augmentation yields consistent improvements in long-tail conversational recommendation.
Towards knowledge-based recom- mender dialog system
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LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems
LumiCRS shows that combining a tailored focal loss, prototype-guided representation learning, and LLM-generated tail dialogue augmentation yields consistent improvements in long-tail conversational recommendation.