Towards Robust and Trustworthy Recommendation Systems

推荐系统（RS）是几个行业的一个组成部分，它包含了增强决策的选择和战略行为。它们通过预测用户感兴趣的项目来减轻信息过载的负担。在电子商务中，成功的推荐代表了交叉销售的巨大潜力，每个客户的平均订单规模增加，客户满意度提高，以及优化资源的控制消费。RS是亚马逊、沃尔玛、Netflix等公司的强大工具。在COVID-19时代，截至2020年4月21日，美国和加拿大的电子商务订单已经呈现出129%的同比增长，在线零售订单总额增长了146%。用户自适应推荐系统在开放平台和信息访问领域的广泛使用，如电子商务，为不道德的用户提供了强烈的动机，将恶意配置文件“先令攻击”注入到RS中，以操纵推荐排名，以获得经济优势。例如，索尼电影公司承认使用虚假的电影评论家的话来宣传一些新上映的电影。亚马逊发现，无良的生产者采取了更具欺骗性的手段，导致了荒谬的推荐结果。eBay不断地与那些破坏系统的用户打交道，他们从其他会员那里购买好的评级，以提高自己的声誉。在存在先令攻击的情况下，通过构建具有稳定推荐的鲁棒系统来保持推荐的公平性是至关重要的。该研究计划的重点是开发和验证新的模型和策略，以解决威胁推荐质量，公平性和操纵用户决策的防御先令攻击的根本问题。特别是，我们的目标是提供两个方面的攻击抵抗过程的建模解决方案和策略：A）先令攻击检测和B）对恶意攻击的鲁棒性。这两个方面对于保持和提高RS的可信度和公平性至关重要。拟议的研究计划包括开发有效的模型和方法，用于发现此类攻击，并使用图论，聚类分析，代数多重网格，深度学习和对抗机器学习的进步来提高RS的鲁棒性。零售业产生的数据量正在呈指数级增长，这为那些努力分析这些数据以获得更好的鲁棒性以获得竞争优势的人带来了挑战和机遇。该研究计划将涉及设计新的分布式/并行网络架构和协议，用于使用多层网络处理大规模数据。总的来说，拟议的研究旨在解决上述挑战，最终建立高效，可扩展和强大的RS通过一个坚实的计划，培训HQP在高度适用和尖端技术。

Recommender systems (RSs) are an integral part of several industries that incorporate choices and strategic behaviours for enhanced decision-making. They reduce the burden of information overload by predicting items of interest to users. In e-commerce, successful recommendations represent tremendous potential for cross-selling and escalated average order sizes per customer, improved customer satisfaction, and controlled consumption for optimizing resources. RSs are powerful tools for Amazon, Walmart, Netflix, and more. In the era of COVID19, by April 21, 2020, U.S. and Canadian e-commerce orders had already exhibited 129% year over year growth, with an impressive 146 % increase in total online retail orders. Widespread use of user-adaptive recommendation systems in open platforms and information access domains, as e-commerce, provides a strong motivation for unscrupulous users to inject malicious profiles "shilling attacks" into the RSs to manipulate recommendation rankings in order to gain economic advantage. For example, Sony Pictures admitted using fake quotes from non-existent movie critics to promote some newly released films. Amazon found that unscrupulous producers were taking a more deceitful artifice, leading to the absurd recommendation results. eBay continually deals with users who subvert the system by purchasing good ratings from other members to bolster their reputations. It is crucial to maintain the fairness of recommendations by constructing a robust system with stable recommendations in the presence of shilling attacks. The focus of this research program is on developing and validating new models and strategies for solving the underlying problems of defending shillings attacks that threaten the recommendation quality, fairness, and manipulate user decisions. In particular, we aim at providing modeling solutions and strategies for two aspects of the attack-resistance process: A) Shilling attack detection; and B) Robustness against malicious attacks. These two aspects are critical for maintaining and increasing the trustworthiness and fairness of RSs. The proposed research program includes the development of effective models and methodologies for discovering such attacks and improving the robustness of RSs using advances in graph theory, clustering analysis, algebraic multigrid, deep learning, and adversarial machine learning. The volume of data produced in the retail industry is expanding exponentially, creating challenges and opportunities for those diligently analyzing these data towards better robustness to gain a competitive advantage. This research program will involve designing new distributed/parallel network architecture and protocols for handling large-scale data using multi-tier networks. Overall, the proposed research aims to solve the above-mentioned challenges for the ultimate establishment of efficient, scalable, and robust RSs through a solid plan of training HQP in highly applicable and cutting-edge techniques.