Many existing algorithms for streaming geometric data analysis have been plagued by
exponential dependencies in the space complexity, which are undesirable for processing …

We present a novel dimensionality reduction method for the approximate maximum inner
product search (MIPS), named CEOs, based on the theory of concomitants of extreme order …

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 …

Top-k recommendation seeks to deliver a personalized list of k items to each individual user.
An established methodology in the literature based on matrix factorization (MF), which …

Due to the wide applications in recommendation systems, multi-class label prediction and
deep learning, the Maximum Inner Product (MIP) search problem has received extensive …

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 $ 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 …

As a kind of generalization of Transformers in the graph domain, Global Graph Transformers
are good at learning distant knowledge by directly doing information interactions on …

The era of huge data necessitates highly efficient machine learning algorithms. Many
common machine learning algorithms, however, rely on computationally intensive …

We propose a novel dimensionality reduction method for maximum inner product search
(MIPS), named CEOs, based on the theory of concomitants of extreme order statistics …