108 citations · 150 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
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…