用检索得分熵判断用户兴趣宽窄，动态调整对话策略。
Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs
- 通过检索得分分布的熵值判断用户兴趣范围
- 低熵时直接推荐，高熵时追问澄清需求
- 无需扩展上下文窗口，适配超大规模商品库
对话式推荐系统在电商场景中具有丰富交互潜力，但在使用大语言模型（LLM）面对海量商品目录时，如何平衡探索（澄清用户需求）与利用（生成推荐）仍是挑战。本文提出基于检索得分分布熵来建模用户兴趣的广度：利用神经检索器获取查询相关商品，计算重排序得分的熵值，动态调控对话策略——低熵（兴趣明确）时触发直接推荐，高熵（兴趣模糊）时生成探索性问题。该方法使LLM驱动的智能体能在不膨胀上下文窗口的前提下，实时感知任意规模商品目录，实现高效对话决策。
原文摘要 · Abstract (English)
Conversational recommender systems promise rich interactions for e-commerce, but balancing exploration (clarifying user needs) and exploitation (making recommendations) remains challenging, especially when deploying large language models (LLMs) with vast product catalogs. We address this challenge by modeling the breadth of user interest via the entropy of retrieval score distributions. Our method uses a neural retriever to fetch relevant items for a user query and computes the entropy of the re-ranked scores to dynamically route the dialogue policy: low-entropy (specific) queries trigger direct recommendations, whereas high-entropy (ambiguous) queries prompt exploratory questions. This simple yet effective strategy allows an LLM-driven agent to remain aware of an arbitrarily large catalog in real-time without bloating its context window.
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