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Mar, 2020
AutoFIS: 基于分解模型的点击率预测自动特征交互选择
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
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Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai...
TL;DR
本文提出了一个自动特征交互选择的两阶段算法(AutoFIS)来改进推荐系统中的CTR预测。该算法可以基于因子分解模型自动识别重要的特征交互,从而显著提高了各种基于FM的模型的性能。
Abstract
Learning effective
feature interactions
is crucial for click-through rate (CTR) prediction tasks in
recommender systems
. In most of the existing deep learning models,
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