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

In recent years, reinforcement learning and bandits have transformed a wide range of real-
world applications including healthcare, finance, recommendation systems, robotics, and …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for
many social computing applications, such as group recommender systems. As the distances …

Conversational recommender systems (CRS) aim to proactively elicit user preference and
recommend high-quality items through natural language conversations. Typically, a CRS …

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

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 …

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

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

Conversational recommender systems (CRS) aim to recommend suitable items to users
through natural language conversations. For developing effective CRSs, a major technical …