542 citations · 1k across the 10 of their papers we have counts for
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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…
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…
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…
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…