Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.

中文翻译：

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这并不总是关于广泛和深入的模型：使用客户行为嵌入表示进行点击率预测

除了自然语言处理和计算机视觉之外，大型学习模型也已进入电子商务领域。特别是对于推荐系统和点击率预测，这些模型表现出了强大的预测能力。在这项工作中，我们的目标是在仅给定当前会话的情况下预测客户点击给定推荐的概率。因此，我们提出了一种由客户行为嵌入表示和循环神经网络组成的两阶段方法。在第一阶段，我们训练一个嵌入客户活动数据的自监督skip-gram。生成的嵌入表示在第二阶段用于对客户序列进行编码，然后将其用作学习模型的输入。我们提出的方法与利用广泛的端到端模型进行点击率预测的流行趋势不同。这些实验结合了现实世界的工业用例和广泛使用且公开可用的基准数据集，表明我们的方法优于当前最先进的模型。我们的方法可以预测客户的点击意图，对于工业用例，平均 F1 准确率为 94%，比最先进的基准高出一个百分点；对于基准数据集，平均 F1 准确率为 79%，这比最先进的基线高出一个百分点。比经过最佳测试的最先进基准高出七个百分点以上。结果表明，与该领域当前的趋势相反，并不总是需要大型端到端模型。我们的实验分析表明，我们的方法之所以有效，是因为我们用作客户表示的客户行为的自我监督预训练嵌入。