Kernkonzepte
Combining Large Language Models (LLMs) with Conversational Recommender Systems (CRSs) can significantly improve their individual performance in understanding and responding to user needs within e-commerce pre-sales dialogues.
Statistiken
BCRS-CLLM achieves a 12.3% improvement over ChatGLM on the average F1 score across all 5 categories for user need elicitation.
ALLM-CCRS achieves 6.9%, 3.8%, and 4.9% improvements on Accuracy, Hit@5, and MRR@5, respectively, compared to UniMIND (CPT) for user needs-based recommendation.
ALLM-BCRS achieves 9.4%, 3.0%, and 6.0% improvements on Accuracy, Hit@5, and MRR@5, respectively, when compared to UniMIND(BART) for user needs-based recommendation.