Biased autoencoder for collaborative filtering with temporal signals
作者：
Highlights：
• Autoencoder can provide more accurate results in Collaborative Filtering.
• Neural networks give satisfying results in rating prediction due to non-linearity.
• Autoencoder has research potential in Collaborative Filtering and rating prediction.
• The usage of Autoencoder and temporal signals jointly give remarkable RMSE scores.
• With the advent of GPUs, neural networks become popular in Collaborative Filtering.
摘要
•Autoencoder can provide more accurate results in Collaborative Filtering.•Neural networks give satisfying results in rating prediction due to non-linearity.•Autoencoder has research potential in Collaborative Filtering and rating prediction.•The usage of Autoencoder and temporal signals jointly give remarkable RMSE scores.•With the advent of GPUs, neural networks become popular in Collaborative Filtering.
论文关键词：Collaborative filtering,AutoRec,Temporal dynamics,Bias,Autoencoder
论文评审过程：Received 4 June 2021, Revised 11 August 2021, Accepted 13 August 2021, Available online 21 August 2021, Version of Record 25 August 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115775
