26 citations · 73 across the 10 of their papers we have counts for
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cs.IR2024★ 16 cited
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…
cs.IR2024
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…
cs.IR2023★ 26 cited
A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu +9
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recomm…