@inproceedings{chen2024onesparse,
author = {Chen, Yaoqi and Zheng, Ruicheng and Chen, Qi and Xu, Shuotao and Zhang, Qianxi and Wu, Xue and Han, Weihao and Yuan, Hua and Li, Mingqin and Wang, Yujing and Li, Jason and Yang, Fan and Sun, Hao and Deng, Weiwei and Sun, Feng and Zhang, Qi and Yang, Mao},
title = {OneSparse: A Unified System for Multi-index Vector Search},
booktitle = {2024 The Web Conference},
year = {2024},
month = {May},
abstract = {Multi-index vector search has become the cornerstone for many applications, such as recommendation systems. Efficient search in such a multi-modal hybrid vector space is challenging since no single index design performs well for all kinds of vector data. Existing approaches to processing multi-index hybrid queries either suffer from algorithmic limitations or processing inefficiency. In this paper, we propose OneSparse, a unified multi-vector index query system that incorporates multiple posting-based vector indices, which enables highly efficient retrieval of multi-modal data-sets. OneSparse introduces a novel multi-index query engine design of inter-index intersection push-down. It also optimizes the vector posting format to expedite multi-index queries. Our experiments show OneSparse achieves more than 6× search performance improvement while maintaining comparable accuracy. OneSparse has already been integrated into Microsoft online web search and advertising systems with 5 × + latency gain for Bing web search and 2.0% Revenue Per Mille (RPM) gain for Bing sponsored search.},
url = {http://approjects.co.za/?big=en-us/research/publication/onesparse-a-unified-system-for-multi-index-vector-search/},
}