The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …

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

Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

Understanding and addressing unfairness in LLMs are crucial for responsible AI
deployment. However, there is a limited number of quantitative analyses and in-depth …

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as
a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback …

Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …