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arXiv · 2509.24632

UniDex: Rethinking Search Inverted Indexing with Unified Semantic Modeling

Zan Li·Jiahui Chen·Yuan Chai·Xiaoze Jiang·Xiaohua Qi·Zhiheng Qin·Runbin Zhou·Shun Zuo·Guangchao Hao·Kefeng Wang·Jingshan Lv·Yupeng Huang·Xiao Liang·Han Li
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Abstract

Inverted indexing has traditionally been a cornerstone of modern search systems, leveraging exact term matches to determine relevance between queries and documents. However, this term-based approach often emphasizes surface-level token overlap, limiting the system's generalization capabilities and retrieval effectiveness. To address these challenges, we propose UniDex, a novel model-based method that employs unified semantic modeling to revolutionize inverted indexing. UniDex replaces complex manual designs with a streamlined architecture, enhancing semantic generalization while reducing maintenance overhead. Our approach involves two key components: UniTouch, which maps queries and documents into semantic IDs for improved retrieval, and UniRank, which employs semantic matching to rank results effectively. Through large-scale industrial datasets and real-world online traffic assessments, we demonstrate that UniDex significantly improves retrieval capabilities, marking a paradigm shift from term-based to model-based indexing. Our deployment within Kuaishou's short-video search systems further validates UniDex's practical effectiveness, serving hundreds of millions of active users efficiently.

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Zan Li, Jiahui Chen, Yuan Chai, Xiaoze Jiang, Xiaohua Qi, Zhiheng Qin, Runbin Zhou, Shun Zuo, Guangchao Hao, Kefeng Wang, Jingshan Lv, Yupeng Huang, Xiao Liang, Han Li. 2025-09-29. UniDex: Rethinking Search Inverted Indexing with Unified Semantic Modeling. https://arxiv.org/abs/2509.24632

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