Automated recommendations can nowadays be found on many e-commerce platforms, and
such recommendations can create substantial value for consumers and providers. Often …

Recommendation systems are dynamic economic systems that balance the needs of
multiple stakeholders. A recent line of work studies incentives from the content providers' …

Research and education in machine learning requires diverse, representative, and open
datasets that contain sufficient samples to handle the necessary training, validation, and …

Preference elicitation plays a central role in interactive recommender systems. Most
preference elicitation approaches use either item queries that ask users to select preferred …

Interactive recommender systems have emerged as a promising paradigm to overcome the
limitations of the primitive user feedback used by traditional recommender systems (eg …

Recommender systems are expected to be assistants that help human users find relevant
information automatically without explicit queries. As recommender systems evolve …

The rise of distributed cloud computing technologies has been pivotal for the large-scale
adoption of Artificial Intelligence (AI) based applications for high fidelity and scalable service …

Probabilistic programming is a growing area that strives to make statistical analysis more
accessible, by separating probabilistic modelling from probabilistic inference. In practice this …

The published method Generative Adversarial Networks for Recommender Systems
(GANRS) allows generating data sets for collaborative filtering recommendation systems …

Preference elicitation plays a central role in interactive recommender systems. Most
preference elicitation approaches use either item queries that ask users to select preferred …