108 citations · 114 across the 4 of their papers we have counts for
4 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…
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…