24 citations · 60 across the 11 of their papers we have counts for
11 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…
Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
Yapeng Li, Bo Jiang, Ziyan Zhang +2
Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-community coordination, and global i…
Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
Beibei Wang, Bo Jiang, Ziyan Zhang +1
Graph Data Prompt (GDP), which introduces specific prompts in graph data for efficiently adapting pre-trained GNNs, has become a mainstream approach to graph fine-tuning learning p…
Feature Staleness Aware Incremental Learning for CTR Prediction
Zhikai Wang, Yanyan Shen, Zibin Zhang +1
Click-through Rate (CTR) prediction in real-world recommender systems often deals with billions of user interactions every day. To improve the training efficiency, it is common to…
Multi-Level Variational Spectroscopy using a Programmable Quantum Simulator
Zhikun Han, Chufan Lyu, Yuxuan Zhou +17
Energy spectroscopy is a powerful tool with diverse applications across various disciplines. The advent of programmable digital quantum simulators opens new possibilities for condu…
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
Bo Jiang, Fei Xu, Ziyan Zhang +2
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning. However,…
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Zhen Tian, Ting Bai, Zibin Zhang +4
With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely incr…
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Yanyan Shen, Lifan Zhao, Weiyu Cheng +3
Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge,…
Unified GCNs: Towards Connecting GCNs with CNNs
Ziyan Zhang, Bo Jiang, Bin Luo
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning. Existing graph convolution layers are mainly des…
Multiple Graph Adversarial Learning
Bo Jiang, Ziyan Zhang, Jin Tang +1
Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming wi…
Graph Learning-Convolutional Networks
Bo Jiang, Ziyan Zhang, Doudou Lin +1
Recently, graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs gene…