通过去偏见的相关性预测改进Uber Eats主页推荐

Uber Eats’ mission is to make eating effortless, at any time, for anyone. The Uber Eats home feed is an important tool for fulfilling this goal, as it aims to provide a magical food browsing experience by leveraging machine learning techniques to build a personalized list of stores for each user. For example, if a user frequently orders sushi dishes, the feed will adapt by showing them more Japanese restaurants, especially those with highly rated sushi dishes. The personalized recommendations may also contain suggestions for similar-but-novel options, like seafood or other Asian cuisines. 

Uber Eats的使命是让用餐变得轻松，随时随地，适合任何人。Uber Eats主页提供了一个重要的工具，旨在通过利用机器学习技术为每个用户构建个性化的商店列表，从而提供一个神奇的食品浏览体验。例如，如果用户经常订购寿司，主页将通过显示更多的日本餐厅，特别是那些评分很高的寿司餐厅来适应用户。个性化推荐还可能包含类似但新颖的选项，如海鲜或其他亚洲菜。

In order to achieve a high quality personalized feed, a metric we prioritize is accurately estimating conversion rates (abbreviated as CVR in the remainder of this blog post), which denotes the probability of eaters ordering from a particular store after it is shown to them on the home feed. In order to estimate this quantity, we resort to an ML model trained on user interaction data such as user impressions, clicks and orders. However, the interaction data itself does not always perfectly reflect our users’ preferences, as it suffers from a wide range of statistical biases. In fact, since ML models are only as good as the data they are trained on, these statistical biases impacting our interaction data can have a strong detrimental effect on the quality of rankings our models generate.  When we use the term bias in this blog we’re referring to statistical bias.

为了实现高质量的个性化推送，我们优先考虑准确估计转化率（在本博客文章的其余部分中缩写为CVR），即在将商店显示给用户后，用户从特定商店下单的概率。为了估计这个数量，我们依靠一个机器学习模型，该模型基于用户的互动数据，如用户的展示、点击和下单。然而，互动数据本身并不总是完全反映我们用户的偏好，因为它受到各种统计偏差的影响。事实上，由于机器学习模型只能与其训练数据一样好，这些影响我们互动数据的统计偏差可能对我们模型生成的排名质量产生严重不利影响。在本博客中，当我们使用偏差一词时，我们指的是统计偏差。

In the recommendation systems literature, multiple biases impacting ranking quality have already been thoroughly studied, such as position bias, tr...