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 …

Traditional recommendation systems estimate user preference on items from past interaction
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …

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

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

The textile and apparel industries have grown tremendously over the past few years.
Customers no longer have to visit many stores, stand in long queues, or try on garments in …

This paper concerns user preference estimation in multi-round conversational recommender
systems (CRS), which interacts with users by asking questions about attributes and …

Static recommendation methods like collaborative filtering suffer from the inherent limitation
of performing real-time personalization for cold-start users. Online recommendation, eg …

Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and better control …

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