A Survey on Concept Factorization: From Shallow to Deep Representation Learning
作者：
Highlights：
• Wepresenta survey on concept factorization: from shallow to deep representation learning.
• We survey the recent advances in CF methodologies and potential benchmarks by categorizing and summarizing the current methods.
• We first review the root CF model, and then explorethe advancement of CF-based representation learning methods ranging from shallow to deep/multilayer cases.
• Finally, we point out some future directions for CF-based representation learning.
• Overall, this survey mainly aims to provide an insightful overview of theoretical basis and current developments in the field of CF.
摘要
•Wepresenta survey on concept factorization: from shallow to deep representation learning.•We survey the recent advances in CF methodologies and potential benchmarks by categorizing and summarizing the current methods.•We first review the root CF model, and then explorethe advancement of CF-based representation learning methods ranging from shallow to deep/multilayer cases.•Finally, we point out some future directions for CF-based representation learning.•Overall, this survey mainly aims to provide an insightful overview of theoretical basis and current developments in the field of CF.
论文关键词：Survey,Concept factorization,Representation learning,Traditional single-layer CF,Deep/multilayer CF
论文评审过程：Received 31 July 2020, Revised 22 January 2021, Accepted 22 January 2021, Available online 16 February 2021, Version of Record 16 February 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102534
