谷歌智能邮箱思想介绍the learning behind gmail priority inbox

The Learning Behind Gmail Priority Inbox

Douglas Aberdeen Ondrej Pacovsky Andrew Slater

Google Inc.

Zurich, Switzerland

{daa,ondrej,aws}@google.com

Abstract

The Priority Inbox feature of Gmail ranks mail by the probability that the user

will perform an action on that mail. Because “importance” is highly personal,

we try to predict it by learning a per-user statistical model, updated as frequently

at scale, performing real-time ranking and near-online updating of millions of models per day sig-

nificantly complicates the problem. The challenges include inferring the importance of mail without

explicit user labelling; finding learning methods that deal with non-stationary and noisy training

data; constructing models that reduce training data requirements; storing and processing terabytes

of per-user feature data; and finally, predicting in a distributed and fault tolerant way.

While ideas were borrowed from the application of ML in Gmail spam detection [6], importance

ranking is harder as users disagree on what is important, requiring a high degree of personalization.

The result is one of the largest and most user facing applications of ML at Google.

2 The Learning Problem

2.1 Features

the user applies to mail using filters. We calculate feature values during ranking and we temporarily

store those values for later learning. Continuous features are automatically partitioned into binary

features using a simple ID3 style algorithm on the histogram of the feature values.

2.2 Importance Metric

A goal of Priority Inbox is to rank without explicit labelling from the user, allowing the system

to work “out-of-the-box”. Importance ground truth is based on how the user interacts with a mail

after delivery. Our goal is to predict the probability that the user will interact with the mail within

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