7 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…
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
Honghao Li, Qiuze Ru, Yiwen Zhang +3
Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR…
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…
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…
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…