In this vision paper, we propose a shift in perspective for improving the effectiveness of similarity search. Rather than focusing solely on enhancing the data quality, particularly machine learning-generated embeddings, we advocate for a more comprehensive approach that also enhances the underpinning search mechanisms. We highlight three novel avenues that call for a redefinition of the similarity search problem: exploiting implicit data structures and distributions, engaging users in an iterative feedback loop, and moving beyond a single query vector. These novel pathways have gained relevance in emerging applications such as large-scale language models, video clip retrieval, and data labeling. We discuss the corresponding research challenges posed by these new problem areas and share insights from our preliminary discoveries.
@article{arxiv.2308.00909,
title = {Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data},
author = {Renzhi Wu and Jingfan Meng and Jie Jeff Xu and Huayi Wang and Kexin Rong},
journal= {arXiv preprint arXiv:2308.00909},
year = {2023}
}