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 …

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 …

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

Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but more …

Entity alignment (EA) is the task to discover entities referring to the same real-world object
from different knowledge graphs (KGs), which is the most crucial step in integrating multi …

Existing recommender systems in the e-commerce domain primarily focus on generating a
set of relevant items as recommendations; however, few existing systems utilize underlying …

There is increasing recognition of the need for human-centered AI that learns from human
feedback. However, most current AI systems focus more on the model design, but less on …

In recent years, the Neurosymbolic framework has attracted a lot of attention in various
applications, from recommender systems and information retrieval to healthcare and …

Knowledge graphs (KG) have become increasingly important to endow modern
recommender systems with the ability to generate traceable reasoning paths to explain the …

Column annotation, the process of annotating tabular columns with labels, plays a
fundamental role in digital marketing data governance. It has a direct impact on how …