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 (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

Recommender systems have become a popular and effective means to manage the ever-
increasing amount of multimedia content available today and to help users discover …

The constantly growing offering of online learning materials to students is making it more
difficult to locate specific information from data pools. Personalization systems attempt to …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

What makes a good recommendation or good list of recommendations? Research into
recommender systems has traditionally focused on accuracy, in particular how closely the …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

We study fairness in collaborative-filtering recommender systems, which are sensitive to
discrimination that exists in historical data. Biased data can lead collaborative-filtering …

Collaborative Filtering (CF) filters the flow of data that can be recommended, by a
Recommender System (RS), to a target user according to his taste and his preferences. The …