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A Comprehensive Survey on Automated Machine Learning for Recommendations
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9781119808602openalex-snapshot
Model‐Based Reinforcement Learning
inferredcites →https://doi.org/10.1002/cem.915openalex-snapshot
The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration
inferredcites →https://doi.org/10.1007/978-0-387-73003-5_196openalex-snapshot
Gaussian Mixture Models
inferredcites →https://doi.org/10.1007/978-0-387-85820-3openalex-snapshot
Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-3-030-05318-5_3openalex-snapshot
Neural Architecture Search
inferredcites →https://doi.org/10.1016/0004-3702(77)90026-1openalex-snapshot
Speech understanding systems
inferredcites →https://doi.org/10.1016/c2009-0-14501-4openalex-snapshot
Uncertainty in Artificial Intelligence
inferredcites →https://doi.org/10.1016/j.artmed.2020.101822openalex-snapshot
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
inferredcites →https://doi.org/10.1016/j.asoc.2021.107356openalex-snapshot
Bayesian neural architecture search using a training-free performance metric
inferredcites →https://doi.org/10.1016/j.camwa.2013.06.031openalex-snapshot
Using reinforcement learning to find an optimal set of features
inferredcites →https://doi.org/10.1016/j.ins.2009.02.014openalex-snapshot
A wrapper method for feature selection using Support Vector Machines
inferredcites →https://doi.org/10.1016/j.knosys.2020.106622openalex-snapshot
AutoML: A survey of the state-of-the-art
inferredcites →https://doi.org/10.1109/icdm.2002.1183878openalex-snapshot
Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002
inferredcites →https://doi.org/10.1109/tevc.2008.927706openalex-snapshot
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
inferredcites →https://doi.org/10.1109/tkde.2020.3008732openalex-snapshot
A Graph Neural Network Framework for Social Recommendations
inferredcites →https://doi.org/10.1109/tkde.2020.3016482openalex-snapshot
AutoHash: Learning Higher-order Feature Interactions for Deep CTR Prediction
inferredcites →https://doi.org/10.1109/tkde.2021.3115477openalex-snapshot
Automated Feature Selection: A Reinforcement Learning Perspective
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/tkde.2022.3186387openalex-snapshot
AutoSrh: An Embedding Dimensionality Search Framework for Tabular Data Prediction
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1137/1.9781611976700openalex-snapshot
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)
inferredcites →https://doi.org/10.1145/2648584.2648589openalex-snapshot
Practical Lessons from Predicting Clicks on Ads at Facebook
inferredcites →https://doi.org/10.1145/3233770openalex-snapshot
Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
inferredcites →https://doi.org/10.1145/3447556.3447567openalex-snapshot
Techniques for Automated Machine Learning
inferredcites →https://doi.org/10.1145/3485447openalex-snapshot
Proceedings of the ACM Web Conference 2022
inferredcites →https://doi.org/10.1145/3579355openalex-snapshot
AutoML for Deep Recommender Systems: A Survey
inferredcites →https://doi.org/10.1177/1042258718760841openalex-snapshot
A New Methodological Frontier in Entrepreneurship Research: Big Data Studies
inferredcites →https://doi.org/10.1613/jair.1.11854openalex-snapshot
Benchmark and Survey of Automated Machine Learning Frameworks
inferredcites →https://doi.org/10.1613/jair.1.13596openalex-snapshot
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
inferredcites →https://doi.org/10.24963/ijcai.2021/637openalex-snapshot
Automated Machine Learning on Graphs: A Survey
inferredcites →https://doi.org/10.3389/fdata.2022.1029307openalex-snapshot
Auto-GNN: Neural architecture search of graph neural networks
inferredcites →https://doi.org/10.3390/computers10020024openalex-snapshot
Automated Machine Learning for Healthcare and Clinical Notes Analysis
inferredcites →https://doi.org/10.48550/arxiv.1206.2944openalex-snapshot
Practical Bayesian Optimization of Machine Learning Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1308.3432openalex-snapshot
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot
Distilling the Knowledge in a Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1609.04747openalex-snapshot
An overview of gradient descent optimization algorithms
inferredcites →https://doi.org/10.48550/arxiv.1705.04977openalex-snapshot
Detecting Statistical Interactions from Neural Network Weights
inferredcites →https://doi.org/10.48550/arxiv.1707.07435openalex-snapshot
Deep Learning based Recommender System: A Survey and New Perspectives
inferredcites →https://doi.org/10.48550/arxiv.1808.05377openalex-snapshot
Neural Architecture Search: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1810.13306openalex-snapshot
Automated Machine Learning: From Principles to Practices
inferredcites →https://doi.org/10.48550/arxiv.1812.07995openalex-snapshot
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search
inferredcites →https://doi.org/10.48550/arxiv.1905.01392openalex-snapshot
A Survey on Neural Architecture Search
inferredcites →https://doi.org/10.48550/arxiv.1906.02287openalex-snapshot
Automated Machine Learning: State-of-The-Art and Open Challenges
inferredcites →https://doi.org/10.48550/arxiv.1909.10072openalex-snapshot
A generalization of regularized dual averaging and its dynamics
inferredcites →https://doi.org/10.48550/arxiv.2003.05689openalex-snapshot
Hyper-Parameter Optimization: A Review of Algorithms and Applications
inferredcites →https://doi.org/10.48550/arxiv.2006.02903openalex-snapshot
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
inferredcites →https://doi.org/10.48550/arxiv.2106.04873openalex-snapshot
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
inferredcites →https://doi.org/10.48550/arxiv.2110.09770openalex-snapshot
AEFE: Automatic Embedded Feature Engineering for Categorical Features
inferredcites →https://doi.org/10.48550/arxiv.2110.14812openalex-snapshot
Differentiable NAS Framework and Application to Ads CTR Prediction
inferredcites →https://doi.org/10.48550/arxiv.2112.09245openalex-snapshot
Automated Deep Learning: Neural Architecture Search Is Not the End
inferredcites →https://doi.org/10.48550/arxiv.2201.05000openalex-snapshot
Automated Reinforcement Learning: An Overview
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-56027-9_9openalex-snapshot
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
inferred← citeshttps://doi.org/10.1007/978-3-032-04403-7_26openalex-snapshot
AutoML: A Tertiary Study of Phases, Methods, Tools, and Frameworks
inferred← citeshttps://doi.org/10.1007/978-981-95-4897-2_50openalex-snapshot
Deep Learning-Based Feature Selection for Movie Recommendation Services
inferred← citeshttps://doi.org/10.1007/978-981-95-9846-5_33openalex-snapshot
AI-Based Micro-Services for Real-Time Analysis of Crowdfunding Campaigns Performance
inferred← citeshttps://doi.org/10.1007/s10489-025-06800-yopenalex-snapshot
Edge-based graph neighbor filtering network for recommendation
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.115450openalex-snapshot
Adversarial contrastive collaborative filtering
inferred← citeshttps://doi.org/10.1088/2631-8695/ae58edopenalex-snapshot
Prediction of tool wear in dicing machine using GRU-Wavelet-Attention-LSTM
inferred← citeshttps://doi.org/10.1109/aece62803.2024.10911816openalex-snapshot
From Information Overload to Intelligent Recommendation: The Power of Large Language Models
inferred← citeshttps://doi.org/10.1109/aidas67696.2025.11213755openalex-snapshot
Revitalizing Local Commerce : A Geofencing-Based Recommendation Platform for Small Business Owners
inferred← citeshttps://doi.org/10.1109/aisummit66170.2025.11411632openalex-snapshot
The Power of Trust in Recommender Systems: Improving Outcomes and Reducing Noise
inferred← citeshttps://doi.org/10.1109/tkde.2025.3610351openalex-snapshot
AEFS: Adaptive Early Feature Selection for Deep Recommender Systems
inferred← citeshttps://doi.org/10.1109/uv63228.2024.11189206openalex-snapshot
Optimizing ETL Pipelines with AI: A Framework for Intelligent Data Integration
inferred← citeshttps://doi.org/10.1145/3580305.3599322openalex-snapshot
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
inferred← citeshttps://doi.org/10.1145/3627673.3679712openalex-snapshot
OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction
inferred← citeshttps://doi.org/10.1145/3637528.3671571openalex-snapshot
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
inferred← citeshttps://doi.org/10.1145/3746027.3755559openalex-snapshot
Learning to Be a Doctor: Searching for Effective Medical Agent Architectures
inferred← citeshttps://doi.org/10.1145/3746252.3760802openalex-snapshot
G2IFS: Global-to-Instance Feature Selection in Deep Recommender System
inferred← citeshttps://doi.org/10.1145/3746252.3761378openalex-snapshot
SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
inferred← citeshttps://doi.org/10.1145/3773078.3831791openalex-snapshot
Automated Selection-based Mixture-of-Experts with Dual-stage Input–Target Pattern Learning for Sequential Recommendation
inferred← citeshttps://doi.org/10.1145/3805712.3809853openalex-snapshot
Differentiable Dual Anchor Negative Sampling for Graph-based Recommendation
inferred← citeshttps://doi.org/10.20944/preprints202507.0515.v1openalex-snapshot
A Predictive Modeling Approach Based on Curriculum Meta-Learning and Attention-Enriched DeepFM for Sparse Data Scenarios
inferred← citeshttps://doi.org/10.2139/ssrn.5341184openalex-snapshot
A Study of Hybrid DeepFM Frameworks Enhanced with Self-Attention and Meta-Learning for Accurate Product Usage Forecasting
inferred← citeshttps://doi.org/10.2139/ssrn.5341186openalex-snapshot
A Comprehensive Review of DeepFM-Based Predictive Models with Integrated Attention Layers and Meta-Learning Approaches
inferred← citeshttps://doi.org/10.2139/ssrn.5341192openalex-snapshot
Development and Validation of a Lightweight DeepFM Model with Attention-Driven Feature Selection and Meta-Learning Tuning
inferred← citeshttps://doi.org/10.2139/ssrn.5341198openalex-snapshot
Exploring the Role of Dual Attention in DeepFM Networks Enhanced by Meta-Learning for E-Commerce Usage Prediction
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