542 citations · 569 across the 5 of their papers we have counts for
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cs.IR2020
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…
cs.LG2020
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…
cs.IR2020
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…