Topic-Initiator: A Proactive Chatbot with Personalized Topic RAG for Enhancing Willingness to Converse

Abstract

Stimulating users’ conversational willingness to converse remains a major challenge in chatbot research. Most existing chatbots respond passively to user inputs, relying on users to select conversation topics, which often reduces their willingness. To address this issue, we propose, Topic-Initiator, a proactive chatbot that initiates conversations with new topics aligned to user interests. It gathers information from external sources (e.g., the web) to obtain potentially novel and engaging topics. To support this capability, we also introduce a novel Retrieval-Augmented Generation (RAG) framework, Personalized-Topic RAG (PT-RAG), designed to retrieve new and interesting topics for each user. Unlike existing RAG methods that fails to surface unseen information, PT-RAG leverages the inference capabilities of Large Language Models (LLMs) to identify content that matches the user’s interests but is not yet known to them. Specifically, PT-RAG estimates a user’s interests and knowledge from past interactions and organizes collected information into categories. Then, it uses an LLM to select a category that matches their interests and obtain information not seen in their knowledge from the selected category. Automatic and human evaluations demonstrate that PT-RAG retrieves new and interesting information more accurately and that Topic-Initiator significantly enhances users’ willingness to converse compared to existing methods.

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