利用深度学习进行交叉销售的优化

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PayPal’s Data Science team leverages big data to empower business decisions and deliver data-driven solutions to better serve our customer’s financial needs and drive business growth. In this article, we introduce a deep learning based framework that can be used to optimize actions for domain-specific objectives.

PayPal的数据科学团队利用大数据赋予业务决策权，并提供数据驱动的解决方案，以更好地服务于客户的财务需求并推动业务增长。在这篇文章中，我们介绍了一个基于深度学习的框架，可用于优化特定领域目标的行动。

Introduction

简介

Many impactful business problems need to be translated into corresponding machine learning tasks. For example, we must be able to recognize fraudulent credit card transactions to prevent loss for both our merchants and consumers. Credit card fraud detection is often translated to a classification task in machine learning.

许多有影响的商业问题都需要转化为相应的机器学习任务。例如，我们必须能够识别欺诈性的信用卡交易，以防止商家和消费者的损失。信用卡欺诈检测通常被转化为机器学习中的分类任务。

Recommending the right financial products to the right customers is another important problem. One way to tackle product recommendation is by computing a propensity score (i.e. the likelihood of adopting a product for a user). Effective product recommendations and promotional strategies are often built based on product propensity scores. Propensity modeling, which determines the best product-customer pairs, is often translated into a classification task.

向合适的客户推荐合适的金融产品是另一个重要问题。解决产品推荐的一个方法是计算倾向性分数（即用户采用产品的可能性）。有效的产品推荐和促销策略往往是建立在产品倾向分数的基础上。确定最佳产品-客户对的倾向性建模通常被转化为一项分类任务。

Classification

种类

The goal of classification is to categorize data points into one of few buckets. For a binary classification problem, there are two buckets, often denoted by 0 or 1 (negative or positive). A trained model, or a classifier, generates a predicted score representing the likelihood of being a 1 (positive case). For example, in credit card fraud detection, each transaction will be categorized into 1 (fraudulent) or 0 (non-fraudulent). A classifier will generate a score for each transaction, and a score closer to 1 sugge...