In today's competitive and fast-evolving business environment, it is a critical time for
organizations to rethink how to make talent-related decisions in a quantitative manner …

This bibliometric analysis delves into the dynamic landscape of Human Resource
Development (HRD) research, exploring trends, collaborative networks, and influential …

In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …

Job interviews are the most widely accepted method for companies to select suitable
candidates, and a critical challenge is finding the right questions to ask job candidates …

With the rapid development of online recruitment platforms, a variety of emerging
recommendation services have been witnessed for benefiting both job seekers and …

Learning and development, or L&D, plays an important role in talent management, which
aims to improve the knowledge and capabilities of employees through a variety of …

The recent development of recommender systems has a focus on collaborative ranking,
which provides users with a sorted list rather than rating prediction. The sorted item lists can …

Recommender systems easily face the issue of user preference shifts. User representations
will become out-of-date and lead to inappropriate recommendations if user preference has …

Collaborative filtering is a critical technique in recommender systems. It has been
increasingly viewed as a conditional generative task for user feedback data, where newly …

Knowledge tracing aims to trace students' evolving knowledge states by predicting their
future performance on concept-related exercises. Recently, some graph-based models have …