From Events to Trending: A Multi-Stage Hotspots Detection Method Based on Generative Query Indexing
Kaichun Wang, Yanguang Chen, Ting Zhang, Mengyao Bao, Keyu Chen, Xu Hu, Yongliang Wang, Jingsheng Yang, Jinsong Zhang, Fei Lu

TL;DR
This paper introduces a multi-stage hotspots detection framework for trending queries in conversational systems, combining offline event-based indexing with online retrieval to improve detection accuracy and user satisfaction.
Contribution
It presents a novel multi-stage detection framework that integrates static event indexing with real-time retrieval, tailored for dialogue systems.
Findings
Significantly outperforms baseline methods in offline evaluations.
Achieves 27% improvement in user satisfaction in online tests.
Effective cold-start strategy with single-recall module.
Abstract
LLM-based conversational systems have become a popular gateway for information access, yet most existing chatbots struggle to handle news-related trending queries effectively. To improve user experience, an effective trending query detection method is urgently needed to enable differentiated processing of such target traffic. However, current research on trending detection tailored to the dialogue system scenario remains largely unexplored, and methods designed for traditional search engines often underperform in conversational contexts due to radically distinct query distributions and expression patterns. To fill this gap, we propose a multi-stage framework for trending detection, which achieves systematic optimization from both offline generation and online identification perspectives. Specifically, our framework first exploits selected hot events to generate index queries,…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Web Data Mining and Analysis · Complex Network Analysis Techniques
