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首页> 外文期刊>Journal of Purdue Undergraduate Research >Predicting Advertisement Clicks Using Deep Networks: Interpreting Deep Learning Models
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Predicting Advertisement Clicks Using Deep Networks: Interpreting Deep Learning Models

机译：使用深度网络预测广告点击：解释深度学习模型

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Deep learning has achieved state-of-the-art results in a variety of tasks such as classifying images and driverless cars. In this paper, I used deep learning to understand consumer product interests. One of the main goals for advertisement agencies is to develop mathematical models to predict whether consumers will click on their advertisement. Achieving the highest click prediction rate means that these agencies can pay to place their online advertisements effectively to target people most interested in their product. Most existing approaches are based on logistic regression or regression tree models (Trofi mov, Kornetova, & Topinskiy, 2012). The model based on deep learning will be discussed to predict the click rate. The data was from the iPinYou competition, where competitors are tasked to build a model that would achieve a high click through rate (CTR). iPinYou provides advertisement data from nine companies. For each instance in the data, various attributes of the person that the electronic advertisement was sent to were provided as well as if the person clicks on the advertisement. I started with exploratory data analysis by splitting data into different seasons, aggregating different advertisers, and cleaning and generating new attributes. I tested my predictive power using a convolutional neural net and a multiple layer perception model. It was shown that the deep learning models have a competitive predictive power and, at the same time, more interpretable for further analysis.
机译：深度学习在各种任务（例如图像分类和无人驾驶汽车）中都取得了最先进的成果。在本文中，我使用深度学习来了解消费产品的兴趣。广告代理商的主要目标之一是开发数学模型，以预测消费者是否会点击其广告。实现最高的点击预测率意味着这些代理商可以付费有效地放置其在线广告，以将最感兴趣的产品定位到目标人群。现有的大多数方法都基于逻辑回归或回归树模型（Trofi mov，Kornetova和Topinskiy，2012年）。将讨论基于深度学习的模型以预测点击率。数据来自iPinYou竞赛，在竞赛中，竞争对手要负责构建可实现高点击率（CTR）的模型。 iPinYou提供来自九家公司的广告数据。对于数据中的每个实例，提供了电子广告发送到的人的各种属性，以及该人是否点击了广告。我从探索性数据分析开始，将数据分成不同的季节，聚集不同的广告商，清理并生成新属性。我使用卷积神经网络和多层感知模型测试了我的预测能力。结果表明，深度学习模型具有竞争性的预测能力，同时，对于进一步的分析更具解释性。

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