Abstract In recent years, Generative Adversarial Networks (GANs) have been successfully
applied to collaborative filtering for implicit feedback. However, the ability of GAN to learn …

Modern recommender systems may output considerably different recommendations due to
small perturbations in the training data. Changes in the data from a single user will alter the …

While the integration of product images enhances the recommendation performance of
visual-based recommender systems (VRSs), this can make the model vulnerable to …

This chapter reviews existing regulations on discrimination, provides a multifaceted
categorization of biases and fairness criteria, discusses techniques to measure popularity …

This chapter outlines potential privacy and security risks inherent in IRRSs. It explores how
personal data and models can be protected and discusses relevant regulations and …

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This chapter discusses recent regulatory and ethical aspects of transparency, clarifies the
relevant terminology, and highlights the benefits, challenges, and barriers to transparency in …

In today's world, we are surrounded by data. Massive amounts of it are stored on private
computers, reside on large-scale servers of big tech companies, or circulate the Web. This …

Recommender systems are ubiquitous. Our digital lives are influenced by their use when, for
instance, we select the news to read, the product to buy, the friend to connect, and the movie …