Information has exploded on the Internet and mobile with the advent of the
big data era. In particular, recommendation systems are widely used to help
consumers who struggle to select the best products among such a large amount of
information. However, recommendation systems are vulnerable to malicious user
biases, such as fake reviews to promote or demote specific products, as well as
attacks that steal personal information. Such biases and attacks compromise the
fairness of the recommendation model and infringe the privacy of users and
systems by distorting data.Recently, deep-learning collaborative filtering
recommendation systems have shown to be more vulnerable to this bias. In this
position paper, we examine the effects of bias that cause various ethical and
social issues, and discuss the need for designing the robust recommendation
system for fairness and stability.

本篇论文讨论了推荐系统中的道德和社会问题，特别是 malicious user bias 所致的偏见和攻击对推荐模型的影响，强调了设计公平和稳定的推荐系统的重要性，尤其针对深度学习协同过滤推荐系统的脆弱性。