108 citations · 150 across the 10 of their papers we have counts for
10 papers
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Mingshi Yan, Fan Liu, Jing Sun +3
In recommender systems, multi-behavior methods have demonstrated their effectiveness in mitigating issues like data sparsity, a common challenge in traditional single-behavior reco…
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Zhiyong Cheng, Jianhua Dong, Fan Liu +3
Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. Howe…
Cluster-based Graph Collaborative Filtering
Fan Liu, Shuai Zhao, Zhiyong Cheng +2
Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to ex…
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
Fan Liu, Yaqi Liu, Huilin Chen +3
Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability…
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
Zhenyang Li, Fan Liu, Yinwei Wei +3
Recommendation algorithms forecast user preferences by correlating user and item representations derived from historical interaction patterns. In pursuit of enhanced performance, m…
Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models
Yu-Wei Zhan, Fan Liu, Xin Luo +3
Human-Object Interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. However, conventional HOI detection methods often struggle to fully…
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…
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…
Learning to Agree on Vision Attention for Visual Commonsense Reasoning
Zhenyang Li, Yangyang Guo, Kejie Wang +3
Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning. A VCR model generally aims at answering a textual questi…
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
Yali Du, Yinwei Wei, Wei Ji +3
The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their…