5 citations · 8 across the 20 of their papers we have counts for
9 papers · 1 filter
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…
Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation
Lei Sang, Yu Wang, Yi Zhang +2
Contrastive Learning (CL)-based recommender systems have gained prominence in the context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of representa…
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…
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
Honghao Li, Yiwen Zhang, Yi Zhang +3
As an important modeling paradigm in click-through rate (CTR) prediction, the Deep & Cross Network (DCN) and its derivative models have gained widespread recognition primarily due…
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Yi Zhang, Lei Sang, Yiwen Zhang
Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation r…
Dual-domain Collaborative Denoising for Social Recommendation
Wenjie Chen, Yi Zhang, Honghao Li +2
Social recommendation leverages social network to complement user-item interaction data for recommendation task, aiming to mitigate the data sparsity issue in recommender systems.…
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…
Generative-Contrastive Heterogeneous Graph Neural Network
Yu Wang, Lei Sang, Yi Zhang +2
Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges. In recent years, inspired by self-supervised learning (SSL)…