In the realm of education, both independent learning and group learning are esteemed as
the most classic paradigms. The former allows learners to self-direct their studies, while the …

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 …

Last year has witnessed the re-flourishment of tag-aware recommender systems supported
by the LLM-enriched tags. Unfortunately, though large efforts have been made, current …

Occupation-specific job tasks (OSTs) refer to the duties, responsibilities, and activities
associated with a particular occupation, which define the core functions and performance …

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal
direction for investors gaining valuable insight to maximize returns. However, existing works …

Recent years have witnessed the great success of the applications of graph convolutional
networks (GCNs) in various scenarios. However, due to the challenging over-smoothing and …

Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex
tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision …

Ability assessment is a critical task in talent recruitment that aims to identify the most suitable
job candidates by evaluating the alignment of their skills with job requirements. Indeed …

Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more
comprehensive item features and user preferences than traditional SR methods, which has …