Recommender systems have been widely used in different application domains including
energy-preservation, e-commerce, healthcare, social media, etc. Such applications require …

With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

Data Poisoning attacks modify training data to maliciously control a model trained on such
data. In this work, we focus on targeted poisoning attacks which cause a reclassification of …

Data poisoning and backdoor attacks manipulate training data in order to cause models to
fail during inference. A recent survey of industry practitioners found that data poisoning is the …

Machine learning (ML) has been universally adopted for automated decisions in a variety of
fields, including recognition and classification applications, recommendation systems …

Recommender systems have become an integral part of online services due to their ability to
help users locate specific information in a sea of data. However, existing studies show that …

Federated learning (FL) is designed to preserve data privacy during model training, where
the data remains on the client side (ie, IoT devices), and only model updates of clients are …

Adversarial machine learning is the research field investigating vulnerabilities inherent to
machine learning systems' design and ways to defend against them. Recently …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …