most citedParallel Knowledge Enhancement based Framework for Multi-behavior Recommendation

49 citations · 64 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

SinkLoRA: Enhanced Efficiency and Chat Capabilities for Long-Context Large Language Models

Hengyu Zhang

Extending the functionality of the Transformer model to accommodate longer sequence lengths has become a critical challenge. This extension is crucial not only for improving tasks…

cs.IR202415 cited

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…

cs.IR2023

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…

cs.IR202349 cited

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…

eess.SP2023

Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI Feedback

Hengyu Zhang, Zhilin Lu, Xudong Zhang +1

In massive multiple-input multiple-output (MIMO) systems under the frequency division duplexing (FDD) mode, the user equipment (UE) needs to feed channel state information (CSI) ba…