542 citations · 566 across the 2 of their papers we have counts for
7 papers
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Weijun Hong, Guilin Li, Weinan Zhang +4
Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…
An Embedding Learning Framework for Numerical Features in CTR Prediction
Huifeng Guo, Bo Chen, Ruiming Tang +3
Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where most deep CTR models follow an Embedding \& Feature Interaction paradigm. However, the maj…
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Bin Liu, Chenxu Zhu, Guilin Li +6
Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manu…
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Mi Luo, Fei Chen, Pengxiang Cheng +4
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every us…
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
Huifeng Guo, Ruiming Tang, Yunming Ye +3
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods have a strong bias…
Federated Meta-Learning with Fast Convergence and Efficient Communication
Fei Chen, Mi Luo, Zhenhua Dong +2
Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world…