Does Negative Sampling Matter? a Review With Insights Into its Theory and Applications.

Abstract

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processing, data mining, and recommender systems. This surge in interest prompts us to question the fundamental impact of negative sampling: Does negative sampling really matter? Is there a general framework that can incorporate all negative sampling methods? In what fields is it applied? Addressing these questions, we propose a general framework that using negative sampling. Delving into the history of negative sampling, we chart its evolution across five distinct trajectories. We dissect and categorize the strategies used to select negative sample candidates, detailing global, local, mini-batch, hop, and memory-based approaches. Our comprehensive review extends to an analysis of current negative sampling methodologies, systematically grouping them into five classifications: static, hard, GAN-based, Auxiliary-based, and In-batch. Beyond detailed categorization, we explore the practical application of negative sampling across various fields. Finally, we briefly discuss open problems and future directions for negative sampling.