A reliable deep representation learning to improve trust-aware recommendation systems

作者：

Highlights：

• A probabilistic model is proposed to evaluate the rating profiles of users.

• A reliable mechanism is proposed to enhance the users’ rating profiles.

• A deep sparse autoencoder is used to extract latent features of users.

• An integration approach is introduced to make recommendations.

摘要

•A probabilistic model is proposed to evaluate the rating profiles of users.•A reliable mechanism is proposed to enhance the users’ rating profiles.•A deep sparse autoencoder is used to extract latent features of users.•An integration approach is introduced to make recommendations.

论文关键词：Recommender system,Deep neural networks,Data sparsity,Reliability,Trust-aware,Collaborative filtering

论文评审过程：Received 10 March 2021, Revised 16 December 2021, Accepted 18 February 2022, Available online 25 February 2022, Version of Record 1 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116697