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 …

A Conversational Recommender System (CRS) offers increased transparency and control to
users by enabling them to engage with the system through a real-time multi-turn dialogue …

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

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

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

Conversational recommender systems (CRS) enable the traditional recommender systems
to explicitly acquire user preferences towards items and attributes through interactive …

We propose a new task of conversational recommendation over multi-type dialogs, where
the bots can proactively and naturally lead a conversation from a non-recommendation …