26 citations · 73 across the 10 of their papers we have counts for
5 papers · 1 filter
Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
Xiao Han, Chen Zhu, Xiao Hu +3
Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment…
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
Xi Chen, Chuan Qin, Chuyu Fang +5
In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills a…
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models
Feihu Jiang, Chuan Qin, Kaichun Yao +4
Efficient knowledge management plays a pivotal role in augmenting both the operational efficiency and the innovative capacity of businesses and organizations. By indexing knowledge…
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen +3
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture colla…
Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach
Feihu Jiang, Chuan Qin, Jingshuai Zhang +6
In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured info…