NBQ: Next-Best-Question for Dynamic Profiling

Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, need a purpose-driven understanding of a person. For this, the most effective path is to ask the Next Best Question (NBQ) at each turn: the question with the highest expected information gain given what has already been learned and the conversation's goal. We propose the NBQ framework, a plug-and-play smart asker that, given any topic, seeds a diverse pool of hundreds to thousands of candidate questions, maintains a compact, continuously updated user state that tracks coverage and confidence, selects the next question after every answer to maximize incremental value within the prespecified turn budget, and finally distills the unstructured Q&A dialogue into a structured, vector-based user profile ready for downstream mining tasks. As a demanding application, we instantiate NBQ for reciprocal matchmaking, where ''reciprocal'' means compatibility must be mutual: i.e., a match between two people is valid only when one fits the other's preferences, and vice versa. Therefore, each person is modeled with two representations: (i) who they are (self-description) and (ii) whom they prefer (counterpart preferences). NBQ asks questions to refine both vectors and thereby increase the probability of a successful match. To scale matching to real-world platforms with millions of concurrent users and continuously updated profiles, we introduce QuickMatch, an efficient retrieval layer that recasts reciprocal matching from quadratic pairwise scoring to approximate vector search. With modest storage overhead, QuickMatch updates each user's top matches in real time. Compared with random or conventional generative questioning baselines, NBQ improves the quality of user profiling by up to 13.6% and 14.0% in terms of AC@T and AR@T. Meanwhile, QuickMatch accelerates retrieval by up to 22.9× while maintaining a high recall of up to 0.989.