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

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 …

A recommender system aims to provide users with personalized online product or service
recommendations to handle the increasing online information overload problem and …

The Internet of Things has facilitated access to a large volume of sensitive information on
each participating object in an ecosystem. This imposes many threats ranging from the risks …

In the era of internet access, recommender systems try to alleviate the difficulty that
consumers face while trying to find items (eg, services, products, or information) that better …

Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the
territory. Instead, it focuses on hierarchical agglomerative clustering, k‐means clustering …

This chapter shows how a system can recommend to a group of users by aggregating
information from individual user models and modelling the users affective state. It …

Social network analysis has gained significant attention in recent years, largely due to the
success of online social networking and media-sharing sites, and the consequent …

Recommender Systems are currently highly relevant for helping users deal with the
information overload they suffer from the large volume of data on the web, and automatically …

In order to generate relevant recommendations, a context-aware recommender system
(CARS) not only makes use of user preferences, but also exploits information about the …