Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

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

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

Industrial recommender systems have been growing increasingly complex, may
involve\emph {diverse domains} such as e-commerce products and user-generated …

Recently, significant progress has been made in sequential recommendation with deep
learning. Existing neural sequential recommendation models usually rely on the item …

In this paper, we present empirical studies on conversational recommendation tasks using
representative large language models in a zero-shot setting with three primary …

Providing user-understandable explanations to justify recommendations could help users
better understand the recommended items, increase the system's ease of use, and gain …

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

The recent success of large language models (LLMs) has shown great potential to develop
more powerful conversational recommender systems (CRSs), which rely on natural …