The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search ~\cite{DBLP:conf/nips/SimhadriWADBBCH21}, this competition addressed filtered search, out-of-distribution data, sparse and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency over industry-standard baselines, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search.
@article{arxiv.2409.17424,
title = {Results of the Big ANN: NeurIPS'23 competition},
author = {Harsha Vardhan Simhadri and Martin Aumüller and Amir Ingber and Matthijs Douze and George Williams and Magdalen Dobson Manohar and Dmitry Baranchuk and Edo Liberty and Frank Liu and Ben Landrum and Mazin Karjikar and Laxman Dhulipala and Meng Chen and Yue Chen and Rui Ma and Kai Zhang and Yuzheng Cai and Jiayang Shi and Yizhuo Chen and Weiguo Zheng and Zihao Wan and Jie Yin and Ben Huang},
journal= {arXiv preprint arXiv:2409.17424},
year = {2024}
}