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 help users find information by recommending content that a user
might not know about, but will hopefully like. Rating-based collaborative filtering …

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 …

Recommender systems are firmly established as a standard technology for assisting users
with their choices; however, little attention has been paid to the application of the user model …

From the earliest days of the field, Recommender Systems research and practice has
struggled to balance and integrate approaches that focus on recommendation as a machine …

Recommender systems help users to find information that best fits their preferences and
needs in an overloaded search space. Most recommender systems research has been …

There is a need for designing educationally oriented recommendations that deal with
educational goals as well as learners' preferences and context in a personalised way. They …

With the advent of large language models, evaluating and benchmarking these systems on
important AI problems has taken on newfound importance. Such benchmarking typically …

Recommender Systems have to deal with a wide variety of users and user types that
express their preferences in different ways. This difference in user behavior can have a …

Online technologies offer great promise to expand models of delivery for therapeutic
interventions to help users cope with increasingly common mental illnesses like anxiety and …