让聊天机器人主动聊起来，提升互动时长21.77%。
PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval
- 基于用户画像与对话上下文，实时检索相关话题
- 对话时长平均提升21.77%，在真实环境稳定运行超30天
- 适合需要持续互动的社交陪伴类应用
社交聊天机器人已成为日常场景中不可或缺的智能伙伴，涵盖情感支持与个人互动。然而，传统被动响应的聊天机器人常需用户主动开启或延续对话，导致参与度下降、对话时间缩短。本文提出PaRT框架，通过个性化实时检索与生成，实现上下文感知的主动对话。PaRT首先将用户画像与对话上下文融合至大语言模型（LLM），初步优化用户查询并识别潜在意图；基于优化后意图，生成个性化对话话题，并作为目标查询从RedNote中检索相关内容；最后，以摘要化内容为引导，生成知识增强且高参与度的回复。该方法已在真实生产环境稳定运行超过30天，对话平均时长提升21.77%。
原文摘要 · Abstract (English)
Social chatbots have become essential intelligent companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive response mechanisms usually rely on users to initiate or sustain dialogues by bringing up new topics, resulting in diminished engagement and shortened dialogue duration. In this paper, we present PaRT, a novel framework enabling context-aware proactive dialogues for social chatbots through personalized real-time retrieval and generation. Specifically, PaRT first integrates user profiles and dialogue context into a large language model (LLM), which is initially prompted to refine user queries and recognize their underlying intents for the upcoming conversation. Guided by refined intents, the LLM generates personalized dialogue topics, which then serve as targeted queries to retrieve relevant passages from RedNote. Finally, we prompt LLMs with summarized passages to generate knowledge-grounded and engagement-optimized responses. Our approach has been running stably in a real-world production environment for more than 30 days, achieving a 21.77\% improvement in the average duration of dialogues.
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