7 citations · 7 across the 8 of their papers we have counts for
8 papers
UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
Honghao Li, Xianquan Wang, Zibin Zhang +3
Ranking is a core stage in online advertising and recommender systems. Modern ranking models increasingly unify sequential modeling and feature interaction, yet many advances rely…
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…
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…
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
Lei Sang, Qiuze Ru, Honghao Li +3
Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions thr…
From Collapse to Stability: A Knowledge-Driven Ensemble Framework for Scaling Up Click-Through Rate Prediction Models
Honghao Li, Lei Sang, Yi Zhang +2
Click-through rate (CTR) prediction plays a crucial role in modern recommender systems. While many existing methods utilize ensemble networks to improve CTR model performance, they…
Large Language Model Aided QoS Prediction for Service Recommendation
Huiying Liu, Zekun Zhang, Honghao Li +2
Large language models (LLMs) have seen rapid improvement in the recent years, and have been used in a wider range of applications. After being trained on large text corpus, LLMs ob…
Adaptive Fusion Self-supervised Learning for Recommendation
Yu Zhang, Lei Sang, Yi Zhang +2
Self-supervised learning (SSL) has recently attracted significant attention in the field of recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due to…
CETN: Contrast-enhanced Through Network for CTR Prediction
Honghao Li, Lei Sang, Yi Zhang +2
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most exist…