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 …

With the recent advances in the field of artificial intelligence, an increasing number of
decision-making tasks are delegated to software systems. A key requirement for the success …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

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 …

How can end users efficiently influence the predictions that machine learning systems make
on their behalf? This paper presents Explanatory Debugging, an approach in which the …

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

The multi-armed bandit is an important framework for balancing exploration with exploitation
in recommendation. Exploitation recommends content (eg, products, movies, music playlists) …

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

The interdisciplinary field of explainable artificial intelligence (XAI) aims to foster human
understanding of black-box machine learning models through explanation-generating …

Recommender systems have become pervasive on the web, shaping the way users see
information and thus the decisions they make. As these systems get more complex, there is …