Nearest neighbor search is a computational primitive whose efficiency is paramount to many
applications. As such, the literature recently blossomed with many works focusing on …

We present Falconn++, a novel locality-sensitive filtering (LSF) approach for approximate
nearest neighbor search on angular distance. Falconn++ can filter out potential far away …

Maximum inner product search (MIPS) in high-dimensional spaces has wide applications
but is computationally expensive due to the curse of dimensionality. Existing studies employ …

This paper investigates a new yet challenging problem called Reverse k-Maximum Inner
Product Search (RkMIPS). Given a query (item) vector, a set of item vectors, and a set of user …

The maximum inner product search (MIPS), which finds the item with the highest inner
product with a given query user, is an essential problem in the recommendation field …

Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications
such as recommendation systems. Given a query vector and $ n $ atom vectors in $ d …

The maximum inner product search (MIPS) problem in high-dimensional vector spaces has
various applications, primarily driven by the success of deep neural network-based …

The $ k $-Maximum Inner Product Search ($ k $ MIPS) serves as a foundational component
in recommender systems and various data mining tasks. However, while most existing $ k …

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal
challenge in the field of machine learning. In recent years, graph-based methods have …

Existing literature on k-Maximum Inner-Product Search has made it a common belief that
tree based methods are less effective in terms of index construction time and query …