论文标题
点击率预测中的功能嵌入
Feature embedding in click-through rate prediction
论文作者
论文摘要
我们应对嵌入功能的挑战，以改善点击率预测过程。我们选择了三个模型：逻辑回归，分解机和深层分解机，因为我们的基准并提出了五个不同的功能嵌入模块：嵌入缩放，FM嵌入，嵌入编码，NN嵌入，NN嵌入和嵌入重新加权模块。嵌入模块是改善基线模型特征嵌入的一种方式，并以端到端方式与其余模型参数一起训练。每个模块分别添加到基线模型中，以获得新的增强模型。我们在用于基准点击率预测模型的公共数据集上测试了增强模型的预测性能。我们的结果表明，几个提出的嵌入模块为预测性能提供了重要的提高，而训练时间的急剧增加。
We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and propose five different feature embedding modules: embedding scaling, FM embedding, embedding encoding, NN embedding and the embedding reweighting module. The embedding modules act as a way to improve baseline model feature embeddings and are trained alongside the rest of the model parameters in an end-to-end manner. Each module is individually added to a baseline model to obtain a new augmented model. We test the predictive performance of our augmented models on a publicly accessible dataset used for benchmarking click-through rate prediction models. Our results show that several proposed embedding modules provide an important increase in predictive performance without a drastic increase in training time.
