论文标题
讨论公平，强大的建议系统设计的攻击和防御措施
Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design
论文作者
论文摘要
随着大数据时代的出现，信息在互联网和移动设备上爆炸。特别是，推荐系统被广泛用于帮助那些难以在如此大量信息中选择最佳产品的消费者。但是，推荐系统容易受到恶意用户偏见的影响，例如促进或降低特定产品的虚假评论以及窃取个人信息的攻击。这种偏见和攻击损害了推荐模型的公平性，并通过扭曲数据来侵犯用户和系统的隐私性。非常深入的协作性过滤推荐系统已显示出更容易受到这种偏见的影响。在该立场论文中，我们研究了引起各种道德和社会问题的偏见的影响，并讨论了设计强大的建议系统以实现公平和稳定的需求。
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.
