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

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 …

Narrative-driven recommendation (NDR) presents an information access problem where
users solicit recommendations with verbose descriptions of their preferences and context, for …

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

Methods for making high-quality recommendations often rely on learning latent
representations from interaction data. These methods, while performant, do not provide …

Although paths of user interests shift in knowledge graphs (KGs) can benefit conversational
recommender systems (CRS), explicit reasoning on KGs has not been well considered in …

Modern recommender systems usually embed users and items into a learned vector space
representation. Similarity in this space is used to generate recommendations, and …

Conversational Recommender Systems (CRSs) fundamentally differ from traditional
recommender systems by interacting with users in a conversational session to accurately …

Providing explanations for recommended items not only allows users to understand the
reason for receiving recommendations but also provides users with an opportunity to refine …