The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS employ advanced graph learning …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

A Conversational Recommender System (CRS) offers increased transparency and control to
users by enabling them to engage with the system through a real-time multi-turn dialogue …

By providing explanations for users and system designers to facilitate better understanding
and decision making, explainable recommendation has been an important research …

With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …

There has been growing attention on fairness considerations recently, especially in the
context of intelligent decision making systems. For example, explainable recommendation …

Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …