Towards Robust and Trustworthy Recommendation Systems

推荐系统（RSs）是几个行业的一个组成部分，它结合了选择和战略行为来增强决策。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，提高了每个客户的平均订单规模，提高了客户满意度，并控制了消费以优化资源。RSs是亚马逊、沃尔玛、Netflix等公司的强大工具。在新冠肺炎时代，截至2020年4月21日，美国和加拿大的电子商务订单已经实现了129%的同比增长，其中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，为肆无忌惮的用户注入恶意配置文件“先令攻击”到RSs中以操纵推荐排名以获得经济优势提供了强大的动机。例如，索尼影业承认使用不存在的影评人的虚假语录来宣传一些新上映的电影。亚马逊发现，不道德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一直在处理那些通过从其他会员那里购买好评来提升自己声誉的颠覆系统的用户。在先令攻击存在的情况下，通过构建具有稳定推荐的鲁棒系统来保持推荐的公平性至关重要。该研究项目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先令攻击的潜在问题。特别是，我们的目标是为两个方面的攻击抵抗过程提供建模解决方案和策略：A)先令攻击检测；B)对恶意攻击的稳健性。这两个方面对于维护和增加RSs的可信度和公平性至关重要。提出的研究计划包括开发有效的模型和方法来发现此类攻击，并利用图论、聚类分析、代数多网格、深度学习和对抗性机器学习方面的进展来提高RSs的鲁棒性。零售业产生的数据量呈指数级增长，这为那些努力分析这些数据以获得更好的稳健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。总体而言，本研究旨在通过高适用性和前沿技术培训HQP的坚实计划，解决上述挑战，最终建立高效、可扩展和健壮的RSs。

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.