The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

Recommender systems have been researched extensively over the past decades. Whereas
several algorithms have been developed and deployed in various application domains …

Deep learning models for natural language processing (NLP) are increasingly adopted and
deployed by analysts without formal training in NLP or machine learning (ML). However, the …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Research on recommender systems typically focuses on the accuracy of prediction
algorithms. Because accuracy only partially constitutes the user experience of a …

Recommender systems have been increasingly used in online services that we consume
daily, such as Facebook, Netflix, YouTube, and Spotify. However, these systems are often …

Personalized recommender systems have become indispensable in today's online world.
Most of today's recommendation algorithms are data-driven and based on behavioral data …

Automated recommendations have become a ubiquitous part of today's online user
experience. These systems point us to additional items to purchase in online shops, they …

Recommender systems usually face the issue of filter bubbles: over-recommending
homogeneous items based on user features and historical interactions. Filter bubbles will …

Traditionally, the field of recommender systems has evaluated the fruits of its labor using
metrics of algorithmic accuracy and precision (see Chap. 8 for an overview of recommender …