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AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Neural Networks: Tricks of the Trade
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Multilayer feedforward networks are universal approximators
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Causality-based CTR prediction using graph neural networks
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Automatic early stopping using cross validation: quantifying the criteria
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A Comprehensive Overhaul of Feature Distillation
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Product-Based Neural Networks for User Response Prediction
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User-Preference Based Knowledge Graph Feature and Structure Learning for Recommendation
inferredcites →https://doi.org/10.1109/tkde.2021.3072345openalex-snapshot
Sparse Graph Attention Networks
inferredcites →https://doi.org/10.1109/tkde.2021.3073717openalex-snapshot
Learning Dynamic User Interest Sequence in Knowledge Graphs for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1109/tkde.2021.3134985openalex-snapshot
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1109/tmm.2024.3394681openalex-snapshot
Part-Aware Correlation Networks for Few-Shot Learning
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Cognition-Driven Structural Prior for Instance-Dependent Label Transition Matrix Estimation
inferredcites →https://doi.org/10.1109/tpds.2015.2427155openalex-snapshot
Minimizing Inter-Server Communications by Exploiting Self-Similarity in Online Social Networks
inferredcites →https://doi.org/10.1109/wacv48630.2021.00360openalex-snapshot
Attentional Feature Fusion
inferredcites →https://doi.org/10.1145/2873055openalex-snapshot
Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation
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Deep Neural Networks for YouTube Recommendations
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Wide & Deep Learning for Recommender Systems
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Neural Factorization Machines for Sparse Predictive Analytics
inferredcites →https://doi.org/10.1145/3124749.3124754openalex-snapshot
Deep & Cross Network for Ad Click Predictions
inferredcites →https://doi.org/10.1145/3159652.3159668openalex-snapshot
Sequential Recommendation with User Memory Networks
inferredcites →https://doi.org/10.1145/3219819.3219823openalex-snapshot
Deep Interest Network for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3298689.3347058openalex-snapshot
Are we really making much progress? A worrying analysis of recent neural recommendation approaches
inferredcites →https://doi.org/10.1145/3336191.3371785openalex-snapshot
Interpretable Click-Through Rate Prediction through Hierarchical Attention
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Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
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Neural Collaborative Filtering vs. Matrix Factorization Revisited
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Graph Structure Learning for Robust Graph Neural Networks
inferredcites →https://doi.org/10.1145/3404835.3463015openalex-snapshot
Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction
inferredcites →https://doi.org/10.1145/3404835.3463116openalex-snapshot
GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction
inferredcites →https://doi.org/10.1145/3437963.3441727openalex-snapshot
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
inferredcites →https://doi.org/10.1145/3442381.3450078openalex-snapshot
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
inferredcites →https://doi.org/10.1145/3459637.3482486openalex-snapshot
Open Benchmarking for Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3488560.3498458openalex-snapshot
Triangle Graph Interest Network for Click-through Rate Prediction
inferredcites →https://doi.org/10.1145/3506715openalex-snapshot
FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data
inferredcites →https://doi.org/10.1145/3511808.3557479openalex-snapshot
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models
inferredcites →https://doi.org/10.1145/3511808.3557541openalex-snapshot
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
inferredcites →https://doi.org/10.1145/3539597.3570372openalex-snapshot
CL4CTR: A Contrastive Learning Framework for CTR Prediction
inferredcites →https://doi.org/10.1145/3539597.3570384openalex-snapshot
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
inferredcites →https://doi.org/10.1145/3539618.3591681openalex-snapshot
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
inferredcites →https://doi.org/10.1145/3564283openalex-snapshot
RESUS: Warm-up Cold Users via Meta-learning Residual User Preferences in CTR Prediction
inferredcites →https://doi.org/10.1145/3573385openalex-snapshot
Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder
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SLED: Structure Learning based Denoising for Recommendation
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Explainable Recommendation: A Survey and New Perspectives
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Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
inferredcites →https://doi.org/10.1609/aaai.v35i5.16561openalex-snapshot
Detecting Beneficial Feature Interactions for Recommender Systems
inferredcites →https://doi.org/10.1609/aaai.v35i5.16600openalex-snapshot
Heterogeneous Graph Structure Learning for Graph Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v37i4.25577openalex-snapshot
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
inferredcites →https://doi.org/10.24963/ijcai.2017/239openalex-snapshot
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
inferredcites →https://doi.org/10.24963/ijcai.2017/435openalex-snapshot
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
inferredcites →https://doi.org/10.48550/arxiv.1611.01144openalex-snapshot
Categorical Reparameterization with Gumbel-Softmax
inferredcites →https://doi.org/10.48550/arxiv.2111.05323openalex-snapshot
Variational Multi-Task Learning with Gumbel-Softmax Priors
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s00521-026-12076-1openalex-snapshot
DaDc: dual attention and dual co-action net for click-through rate prediction
inferred← citeshttps://doi.org/10.1007/s10660-025-10061-9openalex-snapshot
Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives
inferred← citeshttps://doi.org/10.1007/s11227-025-07041-zopenalex-snapshot
Foreground-aware transformer network for person re-identification
inferred← citeshttps://doi.org/10.1109/cscwd64889.2025.11033482openalex-snapshot
Enhancing CTR Prediction with Dynamic Fusion Network
inferred← citeshttps://doi.org/10.1109/tcss.2025.3635214openalex-snapshot
Optimizing Feature Interaction via Information Bottleneck for CTR Prediction
inferred← citeshttps://doi.org/10.1145/3711896.3737106openalex-snapshot
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
inferred← citeshttps://doi.org/10.1145/3746252.3761193openalex-snapshot
Streamlining Feature Interactions via Selectively Crossing Vectors for Click-Through Rate prediction
inferred← citeshttps://doi.org/10.1145/3750725openalex-snapshot
Bottlenecked Heterogeneous Graph Contrastive Learning for Robust Recommendation
inferred← citeshttps://doi.org/10.1145/3763789openalex-snapshot
Self-Monitoring Large Language Models for Click-Through Rate Prediction
inferred← citeshttps://doi.org/10.1145/3799425openalex-snapshot
Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction
inferred← citeshttps://doi.org/10.1145/3848507openalex-snapshot
GraphLLM4CTR: Large Language Model-Enhanced Graph Neural Networks for Click-through Rate Prediction in Recommender Systems
inferred← citeshttps://doi.org/10.3390/jtaer20010012openalex-snapshot
Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings
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