创造性洞察的因果机器学习

A framework to identify the causal impact of successful visual components.

一个用于识别成功视觉组件因果影响的框架。

By Billur Engin, Yinghong Lan, Grace Tang, Cristina Segalin, Kelli Griggs, Vi Iyengar

由Billur Engin，Yinghong Lan，Grace Tang，Cristina Segalin，Kelli Griggs，Vi Iyengar

Introduction

介绍

At Netflix, we want our viewers to easily find TV shows and movies that resonate and engage. Our creative team helps make this happen by designing promotional artwork that best represents each title featured on our platform. What if we could use machine learning and computer vision to support our creative team in this process? Through identifying the components that contribute to a successful artwork — one that leads a member to choose and watch it — we can give our creative team data-driven insights to incorporate into their creative strategy, and help in their selection of which artwork to feature.

在Netflix，我们希望我们的观众能够轻松找到与他们共鸣和吸引力的电视节目和电影。我们的创意团队通过设计最能代表我们平台上每个标题的推广艺术品来实现这一目标。如果我们能够利用机器学习和计算机视觉来支持我们的创意团队进行这个过程会怎样呢？通过识别对成功艺术品有贡献的组件 - 即导致会员选择并观看的艺术品 - 我们可以为我们的创意团队提供数据驱动的见解，以融入他们的创意策略，并帮助他们选择要展示的艺术品。

We are going to make an assumption that the presence of a specific component will lead to an artwork’s success. We will discuss a causal framework that will help us find and summarize the successful components as creative insights, and hypothesize and estimate their impact.

我们将假设特定组件的存在将导致艺术品的成功。我们将讨论一个因果框架，帮助我们找到和总结成功的组件作为创意洞察，并假设和估计它们的影响。

The Challenge

挑战

Given Netflix’s vast and increasingly diverse catalog, it is a challenge to design experiments that both work within an A/B test framework and are representative of all genres, plots, artists, and more. In the past, we have attempted to design A/B tests where we investigate one aspect of artwork at a time, often within one particular genre. However, this approach has a major drawback: it is not scalable because we either have to label images manually or create new asset variants differing only in the feature under investigation. The manual nature of these tasks means that we cannot test many tit...