Does Negative Sampling Matter? A Review with Insights into its Theory and Applications

arXiv:2402.17238v1 Announce Type: new
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 growing interest raises several critical questions: Does negative sampling really matter? Is there a general framework that can incorporate all existing negative sampling methods? In what fields is it applied? Addressing these questions, we propose a general framework that leverages negative sampling. Delving into the …

abstract applications arxiv computer computer vision cs.lg data data mining insights language language processing machine machine learning matter mining natural natural language natural language processing negative processing questions raises recommender systems research review sampling systems theory type vision