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

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

Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. To develop an effective CRS, the support of high-quality …

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

In the era of online business, many e-commerce sites have evolved which recommend items
according to one's needs and interests. Plenty of data is available to be processed to make …

Conversational recommender systems (CRS) aim to provide highquality recommendations
in conversations. However, most conventional CRS models mainly focus on the dialogue …

In Conversational Recommender Systems (CRSs), conversations usually involve a set of
related items and entities eg, attributes of items. These items and entities are mentioned in …

Conversational recommender systems (CRSs) aim to recommend high-quality items to
users through a dialogue interface. It usually contains multiple sub-tasks, such as user …

In recent years, conversational recommender system (CRS) has received much attention in
the research community. However, existing studies on CRS vary in scenarios, goals and …

Sequential recommendation models a user's historical sequence to predict future items.
Existing studies utilize deep learning methods and contrastive learning for data …