49 citations · 87 across the 7 of their papers we have counts for
7 papers
Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In real-world recommendation scenarios, users typically engage with platforms through multiple types of behavioral interactions. Multi-behavior recommendation algorithms aim to lev…
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogen…
Deep Pattern Network for Click-Through Rate Prediction
Hengyu Zhang, Junwei Pan, Dapeng Liu +2
Click-through rate (CTR) prediction tasks play a pivotal role in real-world applications, particularly in recommendation systems and online advertising. A significant research bran…
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Hengyu Zhang, Chang Meng, Wei Guo +5
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User be…
Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
Chang Meng, Chenhao Zhai, Yu Yang +2
Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation…
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Hengyu Zhang, Enming Yuan, Wei Guo +6
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing beha…
Attention Spiking Neural Networks
Man Yao, Guangshe Zhao, Hengyu Zhang +5
Benefiting from the event-driven and sparse spiking characteristics of the brain, spiking neural networks (SNNs) are becoming an energy-efficient alternative to artificial neural n…