PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval

Zihan Niu, Zheyong Xie, Shaosheng Cao, Chonggang Lu, Zheyu Ye, Tong Xu, Zuozhu Liu, Yan Gao, Jia Chen, Zhe Xu, Yi Wu, Yao Hu

Abstract: Social chatbots have become essential 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 underlying intents for the upcoming conversation. Guided by refined intents, the LLM generates personalized dialogue topics 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.

External IDs:doi:10.1145/3726302.3731946