In the last few years, a renewed interest of the research community in conversational
recommender systems (CRSs) has been emerging. This is likely due to the massive …

In the last few years, a renewed interest of the research community on conversational
recommender systems (CRSs) is emerging. This is probably due to the great diffusion of …

Recommender systems have become ubiquitous in daily life, but their limitations in
interacting with human users have become evident. Deep learning approaches have led to …

Over the last years, we have been witnessing the advent of more and more precise and
powerful recommendation algorithms and techniques able to effectively assess users' tastes …

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

Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …

Recommender systems have demonstrated great success in information seeking. However,
traditional recommender systems work in a static way, estimating user preferences on items …

Over the past decade a significant amount of recommender systems research has
demonstrated the benefits of conversational architectures that employ critique-based …

ABSTRACT Conversational Recommender Systems (CoRSs) implement a paradigm where
users can interact with the system for defining their preferences and discovering items that …

Recommender systems are embracing conversational technologies to obtain user
preferences dynamically, and to overcome inherent limitations of their static models. A …