LatentCRS：一种用于桥接基于LLM的对话推荐中语义与行为的变分EM框架
LatentCRS: A Variational EM Framework for Bridging Semantics and Behavior in LLM-based Conversational Recommendation
AI总结：
提出LatentCRS框架，通过变分EM过程桥接LLM的语义空间与用户行为模式，解决对话推荐中语义理解与推荐精度不匹配的问题，实验表明其有效提升推荐性能。
摘要：
基于大型语言模型（LLM）的对话推荐系统（CRS）使用户能够表达明确和动态的偏好，克服了固定模板的限制。然而，尽管LLM具有卓越的语义能力，它们在推荐准确性方面尚未实现相应的提升。这种差异源于一个基本的表示鸿沟：LLM在语义空间中运行，但缺乏编码用户行为模式（如物品共现）所需的行为基础，而这些对于准确推荐至关重要。为了解决这个问题，我们提出了一种模型无关的变分EM框架，用于在基于LLM的对话推荐中桥接语义与行为（LatentCRS）。基于对话和交互反映相同潜在意图的观察，LatentCRS使用变分期望最大化（EM）过程，其中用户意图连接语义表示与行为模式。在真实世界数据集上的大量实验表明，LatentCRS有效桥接了表示鸿沟，并优于基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
