1 citations · 1 across the 6 of their papers we have counts for
6 papers
PRIME: Mitigating Subgroup Optimization Competition in Shared CTR Top Networks with Plug-in Residual Input-Conditioned Mixture of Expert
Heng Yao, Siyun Hou, Tianying Liu +8
Click-through rate (CTR) models vary in feature-interaction design, yet their top networks usually remain a single multilayer perceptron shared by all examples. Heterogeneous user,…
IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation
Zixuan Li, Haokun Lin, Yicheng Xiao +10
Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object coun…
MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation
Wenzhuo Cheng, Ke Ding, Xin Dong +3
Conversion rate (CVR) prediction models play a vital role in recommendation and advertising systems. Recent research on multi-scenario recommendation shows that learning a unified…
Trie-Aware Transformers for Generative Recommendation
Zhenxiang Xu, Jiawei Chen, Sirui Chen +5
Generative recommendation (GR) aligns with advances in generative AI by casting next-item prediction as token-level generation rather than score-based ranking. Most GR methods adop…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs
Loc Hoang, Rita Brugarolas Brufau, Ke Ding +1
We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with…