A survey for trust-aware recommender systems: A deep learning perspective

作者：

Highlights：

• A systemic survey of current deep learning-based trust-aware recommendation methods from three aspects: social-awareness, robustness, and explainability.

• A comprehensive overview and comment of the key research issues, and state-of-the-art techniques.

• A summary and discussion of promising future directions to addressing remaining challenges.

摘要

•A systemic survey of current deep learning-based trust-aware recommendation methods from three aspects: social-awareness, robustness, and explainability.•A comprehensive overview and comment of the key research issues, and state-of-the-art techniques.•A summary and discussion of promising future directions to addressing remaining challenges.

论文关键词：Recommender systems,Deep learning,Systematic survey

论文评审过程：Received 14 December 2021, Revised 7 April 2022, Accepted 28 April 2022, Available online 8 May 2022, Version of Record 20 May 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108954