Optimizing the Number of Clusters for Billion-Scale Quantization-Based Nearest Neighbor Search

Approximate nearest neighbor search (ANNS) is crucial in various real-world applications, including recommendation systems, data mining, and image retrieval. To date, quantization-based algorithms have emerged as one of the most efficient solutions for ANNS on billion-scale datasets. However, the determination of the optimal number of clusters, a critical factor for peak data performance in quantization-based systems, remains inadequately explored. Previous works often propose numbers of clusters that are not optimal, and the absence of effective methodologies for tuning this parameter leads to suboptimal search performance due to the vast configuration space. In response to this challenge, this paper introduces a novel algorithm that automatically identifies the optimal number of clusters for billion-scale, quantization-based ANNS systems to maximize search efficiency. We propose an analytical model for evaluating retrieval performance, serving as the benchmark for optimizing cluster numbers in quantization-based indexes. Our algorithm applies iterative local adjustments to the ANNS index being constructed, progressively refining the number of clusters. We demonstrate the efficacy of our approach using the popular inverted index structure in quantization-based ANNS systems. Our findings indicate that: (1) By optimizing the number of clusters, the vanilla inverted index exhibits improved retrieval performance on billion-scale datasets when compared to existing state-of-the-art quantization-based methods; and (2) The additional computational overhead introduced by our optimization algorithm is minimal, even when applied to billion-scale datasets.