Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Advances in artificial intelligence (AI) enable new ways of exercising and experiencing
power by automating interpersonal tasks such as interviewing and hiring workers, managing …

Conversational recommender systems (CRSs) provide recommendations through
interactive conversations. CRSs typically provide recommendations through relatively …

Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top
item preferences in a cold-start setting is a key challenge for building effective and …

Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …

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 …

Evidence-based decision-making entails collecting (costly) observations about an
underlying phenomenon of interest, and subsequently committing to an (informed) decision …

Identifying the preferences of a given user through elicitation is a central part of multi-criteria
decision aid (MCDA) or preference learning tasks. Two classical ways to perform this …

This paper introduces an interactive framework to guide decision-makers in a multi-criteria
supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit …