Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes
ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS …

We propose CXL-ANNS, a software-hardware collaborative approach to enable highly
scalable approximate nearest neighbor search (ANNS) services. To this end, we first …

With the development of learning-based embedding models, embedding vectors are widely
used for analyzing and searching unstructured data. As vector collections exceed billion …

Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines
spanning data mining and artificial intelligence, from information retrieval and computer …

In this paper, we investigate the problem of unsupervised domain adaptive hashing, which
leverage knowledge from a label-rich source domain to expedite learning to hash on a label …

Vector quantization is a fundamental operation for data compression and vector search. To
obtain high accuracy, multi-codebook methods represent each vector using codewords …

This paper introduces an efficient and robust framework for hybrid query (HQ) processing,
which combines approximate nearest neighbor search (ANNS) with attribute constraint. HQ …

Abstract The SISAP 2024 Indexing Challenge invited replicable and competitive
approximate similarity search solutions for datasets of up to 100 million real-valued vectors …

The statistical distribution of content uploaded and searched on media sharing sites
changes over time due to seasonal, sociological and technical factors. We investigate the …