108 citations · 134 across the 14 of their papers we have counts for
29 papers
Semantic-Guided Feature Distillation for Multimodal Recommendation
Fan Liu, Huilin Chen, Zhiyong Cheng +2
Multimodal recommendation exploits the rich multimodal information associated with users or items to enhance the representation learning for better performance. In these methods, e…
Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
Harry Cheng, Yangyang Guo, Liqiang Nie +2
Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets. Current methods primarily aim to eithe…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Jing Sun +2
Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfacto…
Multi-Behavior Recommendation with Cascading Graph Convolution Networks
Zhiyong Cheng, Sai Han, Fan Liu +3
Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regard…
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Fan Liu, Zhiyong Cheng, Huilin Chen +3
Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when c…