让对话机器人主动感知情绪并调用知识，实现更打动人的营销对话
Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue
- 构建主动更新情绪与事实信息的多模态认知网络
- 在真实营销数据上提升情绪一致性26%、说服成功率19%
- 适合需要高情感共鸣的电商客服、智能导购等场景
大型语言模型虽使对话系统更加流畅，但在情绪丰富、目标导向的营销场景中仍多为被动响应。为此，我们提出AffectMind，一种具备主动推理与动态知识锚定能力的多模态情感对话代理，以维持情绪一致且具有说服力的互动。该系统包含三个组件：持续从文本、视觉和语调中更新事实与情感上下文的主动知识锚定网络（PKGN）；联合建模用户情绪与购买意图以调整说服策略的情绪-意图对齐模型（EIAM）；以及通过用户反馈强化信号优化情绪连贯性与参与度的强化话语环（RDL）。在两个新构建的营销对话数据集MM-ConvMarket和AffectPromo上的实验表明，AffectMind在情绪一致性（+26%）、说服成功率（+19%）和长期用户参与度（+23%）方面均优于强基线模型，凸显了情感化主动性的商业多模态代理的关键作用。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have enabled fluent dialogue systems, but most remain reactive and struggle in emotionally rich, goal-oriented settings such as marketing conversations. To address this limitation, we propose AffectMind, a multimodal affective dialogue agent that performs proactive reasoning and dynamic knowledge grounding to sustain emotionally aligned and persuasive interactions. AffectMind combines three components: a Proactive Knowledge Grounding Network (PKGN) that continuously updates factual and affective context from text, vision, and prosody; an Emotion--Intent Alignment Model (EIAM) that jointly models user emotion and purchase intent to adapt persuasion strategies; and a Reinforced Discourse Loop (RDL) that optimizes emotional coherence and engagement via reinforcement signals from user responses. Experiments on two newly curated marketing dialogue datasets, MM-ConvMarket and AffectPromo, show that AffectMind outperforms strong LLM-based baselines in emotional consistency (+26\%), persuasive success rate (+19\%), and long-term user engagement (+23\%), highlighting emotion-grounded proactivity as a key capability for commercial multimodal agents.
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