542 citations · 1k across the 10 of their papers we have counts for
10 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…
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
Chenxu Zhu, Bo Chen, Weinan Zhang +5
Feature embedding learning and feature interaction modeling are two crucial components of deep models for Click-Through Rate (CTR) prediction. Most existing deep CTR models suffer…
On Effective Scheduling of Model-based Reinforcement Learning
Hang Lai, Jian Shen, Weinan Zhang +5
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency. Despite its impressive success so far, it is still unclear how to appropriate…
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…
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Huifeng Guo, Ruiming Tang, Yunming Ye +2
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a str…
A Graph-based Push Service Platform
Huifeng Guo, Ruiming Tang, Yunming Ye +2
It is well known that learning customers' preference and making recommendations to them from today's information-exploded environment is critical and non-trivial in an on-line syst…