Web: http://arxiv.org/abs/2209.10117

Sept. 22, 2022, 1:11 a.m. | Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv recommender systems survey systems trustworthy

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