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 …

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 …

With the wide application of recommender systems, the potential impacts of recommender
systems on customers, item providers and other parties have attracted increasing attention …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

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

Recent advancements in foundation models such as large language models (LLM) have
propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is …

Cross-domain recommendation (CDR) aims to provide better recommendation results in the
target domain with the help of the source domain, which is widely used and explored in real …

Abstract Graph Neural Network (GNN) models have been extensively researched and
utilised for extracting valuable insights from graph data. The performance of fairness …

Adapters, a plug-in neural network module with some tunable parameters, have emerged as
a parameter-efficient transfer learning technique for adapting pre-trained models to …

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 …