智能对话中动态生成最优提问，提升用户画像精度与匹配效率。
NBQ: Next-Best-Question for Dynamic Profiling
- 基于信息增益构建可插拔提问框架，实时更新用户状态
- 在匹配任务中提升画像质量13.6%~14.0%，检索速度提升22.9倍
- 适用于招聘、社交等需双向匹配的场景，适合高并发应用
许多现实中的对话场景（如播客、招聘筛选、市场交易）需要以目标为导向理解个人。本文研究「下一最佳问题」（NBQ）问题：每轮对话中，应选择在已知信息和对话目标下预期信息增益最高的问题。我们提出NBQ框架——通过生成多样候选问题、维护紧凑连续更新的用户状态、在预算内自适应选择下一个问题，并将自由对话转化为结构化向量画像。作为典型应用场景，我们将其用于互惠匹配，其中兼容性需双向满足，每个人由自我描述和对方偏好表征建模。为支持大规模匹配，进一步引入QuickMatch高效检索层，将二次配对评分转为近似向量搜索。实验表明，NBQ在AC@T和AR@T上分别提升用户画像质量13.6%和14.0%，而QuickMatch使检索加速22.9倍，召回率达0.989。
原文摘要 · Abstract (English)
Many real-world conversational settings for knowledge discovery, including podcasts, hiring screens, and marketplaces, require a purpose-driven understanding of a person. We study the Next-Best-Question (NBQ) problem: at each turn, an interviewer should ask the question with the highest expected information gain given what has already been learned and the conversation goal. We propose NBQ, a plug-and-play framework that seeds a diverse pool of candidate questions, maintains a compact and continuously updated user state, adaptively selects the next question within a turn budget, and distills the resulting free-form dialogue into a structured vector-based user profile. As a demanding application, we instantiate NBQ for reciprocal matchmaking, where compatibility must be mutual and each person is modeled by both self-description and counterpart-preference representations. To support large-scale matching, we further introduce QuickMatch, an efficient retrieval layer that recasts reciprocal matching from quadratic pairwise scoring to approximate vector search. Experiments show that NBQ improves user profiling quality by up to 13.6% and 14.0% in AC@T and AR@T, respectively, while QuickMatch accelerates retrieval by up to 22.9x with recall up to 0.989.
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