{"ID":2863516,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24632","arxiv_id":"2509.24632","title":"UniDex: Rethinking Search Inverted Indexing with Unified Semantic Modeling","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.","short_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 effectivene...","url_abs":"https://arxiv.org/abs/2509.24632","url_pdf":"https://arxiv.org/pdf/2509.24632v1","authors":"[\"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\"]","published":"2025-09-29T11:41:12Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[]","has_code":false}