Abstract
With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.
Highlights
Online advertising has become the new mode of businesses in recent years, which can make full use of customers’ digital traces to achieve more precise outputs
Leader (FTRL) [11] and Deep&Cross Network (DCN) [18], this paper proposes an integrated model with feature optimization (FO), namely FO-Follow The Regularized Leader (FTRL)-DCN, which will be empowered jointly by the embedding based on DCN, Synthetic Minority Oversampling Technique (SMOTE) on the imbalance of data and the FTRL optimization algorithm [11]
The proposed FO-FTRL-DCN mode for Click-Through Rates (CTR) prediction was evaluated on the famous benchmark of iPinYou https://contest.ipinyou.com/
Summary
Online advertising has become the new mode of businesses in recent years, which can make full use of customers’ digital traces to achieve more precise outputs. The Product-based Neural Network (PNN) model is put forward in Reference [16] to predict the click through rate. Google proposed Deep&Cross Network (DCN) model, in Reference [18], for click rate prediction and personalized recommendations. The model is equipped with a cross network, which can effectively learn the feature interactions and greatly improve the effect of click rate prediction and personalized recommendations. Alibaba introduced Deep Interest Network (DIN), in Reference [19] This model captures the diversified interests of users as interest distribution, adopts attention mechanism to improve the weight of relevant and effective interests, and reduces the impact of unrelated interests. Compared with traditional machine learning algorithms, such as logistic regression and factorization machine, DNN-based CTR model can better deal with the combination of feature vectors and higher-order features, and it has structural advantages in improving prediction accuracy.
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