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 …

Recent advances in Large Language Models (LLMs) have been changing the paradigm of
Recommender Systems (RS). However, when items in the recommendation scenarios …

Sequential recommendation is a crucial task in understanding users' evolving interests and
predicting their future behaviors. While existing approaches on sequence or graph modeling …

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of
recommender systems. Due to its rich semantic content and associations among interactive …

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 …

Digitalization, automation, robotization and green transition are key current drivers changing
the labour markets and the structure of skills needed to perform tasks within jobs. Mitigating …

Occupational skill demand (OSD) forecasting seeks to predict dynamic skill demand specific
to occupations, beneficial for employees and employers to grasp occupational nature and …

Collaborative filtering methods based on graph neural networks (GNNs) have witnessed
significant success in recommender systems (RS), capitalizing on their ability to capture …

Career planning consists of a series of decisions that will significantly impact one's life.
However, current recommendation systems have serious limitations, including the lack of …

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 …