A Comprehensive Survey on Trustworthy Recommender Systems
- URL: http://arxiv.org/abs/2209.10117v1
- Date: Wed, 21 Sep 2022 04:34:17 GMT
- Title: A Comprehensive Survey on Trustworthy Recommender Systems
- Authors: Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin
Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li
- Abstract summary: We provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects.
For each aspect, we summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems.
- Score: 32.523177842969915
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As one of the most successful AI-powered applications, recommender systems
aim to help people make appropriate decisions in an effective and efficient
way, by providing personalized suggestions in many aspects of our lives,
especially for various human-oriented online services such as e-commerce
platforms and social media sites. In the past few decades, the rapid
developments of recommender systems have significantly benefited human by
creating economic value, saving time and effort, and promoting social good.
However, recent studies have found that data-driven recommender systems can
pose serious threats to users and society, such as spreading fake news to
manipulate public opinion in social media sites, amplifying unfairness toward
under-represented groups or individuals in job matching services, or inferring
privacy information from recommendation results. Therefore, systems'
trustworthiness has been attracting increasing attention from various aspects
for mitigating negative impacts caused by recommender systems, so as to enhance
the public's trust towards recommender systems techniques. In this survey, we
provide a comprehensive overview of Trustworthy Recommender systems (TRec) with
a specific focus on six of the most important aspects; namely, Safety &
Robustness, Nondiscrimination & Fairness, Explainability, Privacy,
Environmental Well-being, and Accountability & Auditability. For each aspect,
we summarize the recent related technologies and discuss potential research
directions to help achieve trustworthy recommender systems in the future.
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