7 papers · 1 filter
All-in-one Graph-based Indexing for Hybrid Search on GPUs
Zhonggen Li, Yougen Li, Yifan Zhu +3
Hybrid search has emerged as a promising paradigm that combines lexical and semantic retrieval, enhancing accuracy for applications such as recommendations, information retrieval,…
Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Mengzhao Wang, Boyu Tan, Yunjun Gao +5
Hybrid search, the integration of lexical and semantic retrieval, has become a cornerstone of modern information retrieval systems, driven by demanding applications like Retrieval-…
Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search
Zhonggen Li, Xiangyu Ke, Yifan Zhu +3
Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficie…
Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search
Tingyang Chen, Cong Fu, Xiangyu Ke +3
Maximum Inner Product Search (MIPS) is a fundamental challenge in machine learning and information retrieval, particularly in high-dimensional data applications. Existing approache…
Maximum Inner Product is Query-Scaled Nearest Neighbor
Tingyang Chen, Cong Fu, Kun Wang +5
Maximum Inner Product Search (MIPS) for high-dimensional vectors is pivotal across databases, information retrieval, and artificial intelligence. Existing methods either reduce MIP…
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities
Jiancheng Ruan, Tingyang Chen, Renchi Yang +2
Approximate Nearest Neighbor Search (ANNS) in high-dimensional spaces finds extensive applications in databases, information retrieval, recommender systems, etc. While graph-based…