让大模型对话理解与用户行为数据联动，提升推荐准确率
LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation
- 用变分期望最大化框架连接对话语义与用户行为模式
- 在真实数据集上显著优于基线模型，提升推荐精度
- 适合想融合语言理解与行为建模的推荐系统研究者
基于大语言模型（LLM）的对话推荐系统（CRS）能捕捉用户动态偏好，突破固定模板限制。然而，尽管其语义理解能力出色，推荐准确率并未同步提升。根本原因在于：LLM运行于语义空间，缺乏对用户行为模式（如物品共现）的建模。为此，我们提出模型无关的变分期望最大化框架LatentCRS，利用对话与交互反映相同潜在意图的观察，通过变分EM过程将用户意图作为桥梁，连接语义表征与行为模式。在真实数据集上的大量实验表明，LatentCRS有效弥合了表征鸿沟，显著优于现有基线。
原文摘要 · Abstract (English)
Conversational Recommender Systems (CRS) powered by Large Language Models (LLMs) enable users to articulate explicit and dynamic preferences, overcoming the limitations of fixed templates. However, despite their superior semantic proficiency, LLMs have not yet achieved corresponding improvements in recommendation accuracy. This discrepancy arises from a fundamental representation gap: while LLMs operate within a semantic space, they lack the behavioral grounding needed to encode user behavioral patterns, such as item co-occurrences, which are crucial for accurate recommendations. To address this, we propose a model-agnostic Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation (LatentCRS). Based on the observation that dialogue and interactions reflect the same latent intent, LatentCRS uses a variational expectation-maximization (EM) procedure, where user intent connects semantic representations with behavioral patterns. Extensive experiments on real-world datasets demonstrate that LatentCRS effectively bridges the representation gap and outperforms baselines.
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