1 citations · 1 across the 14 of their papers we have counts for
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DIAURec: Dual-Intent Space Representation Optimization for Recommendation
Yu Zhang, Yiwen Zhang, Yi Zhang +1
General recommender systems deliver personalized services by learning user and item representations, with the central challenge being how to capture latent user preferences. Howeve…
From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
Ziang Lu, Lei Sang, Lin Mu +1
Cross-domain Recommendation (CDR) exploits multi-domain correlations to alleviate data sparsity. As a core task within this field, inter-domain recommendation focuses on predicting…
Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation
Lei Sang, Yu Wang, Yiwen Zhang
Heterogeneous graph neural networks (HGNNs) have demonstrated their superiority in exploiting auxiliary information for recommendation tasks. However, graphs constructed using meta…
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
Honghao Li, Yiwen Zhang, Yi Zhang +2
Hadamard Product (HP) has long been a cornerstone in click-through rate (CTR) prediction tasks due to its simplicity, effectiveness, and ability to capture feature interactions wit…
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
Honghao Li, Hanwei Li, Jing Zhang +4
Multimodal click-through rate (CTR) prediction is a key technique in industrial recommender systems. It leverages heterogeneous modalities such as text, images, and behavioral logs…
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
Yu Zhang, Yiwen Zhang, Yi Zhang +2
Personalized recommendation is widely used in the web applications, and graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems, primarily…