Click-through rate (CTR) prediction is a crucial task in online display
advertising. The embedding-based neural networks have been proposed to learn
both explicit feature interactions through a shallow component and deep feature
interactions using a deep neural network (DNN) component. These sophisticated
models, however, slow down the prediction inference by at least hundreds of
times. To address the issue of significantly increased serving delay and high
memory usage for ad serving in production, this paper presents
\emph{DeepLight}: a framework to accelerate the CTR predictions in three
aspects: 1) accelerate the model inference via explicitly searching informative
feature interactions in the shallow component; 2) prune redundant layers and
parameters at intra-layer and inter-layer level in the DNN component; 3)
promote the sparsity of the embedding layer to preserve the most discriminant
signals. By combining the above efforts, the proposed approach accelerates the
model inference by 46X on Criteo dataset and 27X on Avazu dataset without any
loss on the prediction accuracy. This paves the way for successfully deploying
complicated embedding-based neural networks in production for ad serving.

本文介绍了一种名为 DeepLight 的框架，用于加速 CTR 预测，该框架通过显式地搜索浅层组件中的信息交互、裁剪深度神经网络（DNN）组件中的冗余层和参数以及提高嵌入层稀疏性的方式，成功地将模型推断速度提高了 46 倍，在不降低预测准确性的情况下，有望在广告投放生产中成功部署复杂的基于嵌入的神经网络。

DeepLight: 基于轻量级深度学习特征交互技术的广告 CTR 加速预测

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR  Predictions in Ad Serving

A large fraction of online display advertising is sold via guaranteed
contracts: a publisher guarantees to the advertiser a certain number of user
visits satisfying the targeting predicates of the contract. The publisher is
then tasked with solving the ad serving problem - given a user visit, which of
the thousands of matching contracts should be displayed, so that by the
expiration time every contract has obtained the requisite number of user
visits. The challenges of the problem come from (1) the sheer size of the
problem being solved, with tens of thousands of contracts and billions of user
visits, (2) the unpredictability of user behavior, since these contracts are
sold months ahead of time, when only a forecast of user visits is available and
(3) the minute amount of resources available online, as an ad server must
respond with a matching contract in a fraction of a second.
We present a solution to the guaranteed delivery ad serving problem using
{\em compact allocation plans}. These plans, computed offline, can be
efficiently queried by the ad server during an ad call; they are small, using
only O(1) space for contract; and are stateless, allowing for distributed
serving without any central coordination. We evaluate this approach on a real
set of user visits and guaranteed contracts and show that the compact
allocation plans are an effective way of solving the guaranteed delivery ad
serving problem.

该研究提出采用紧凑的分配计划解决在线展示广告中的保证交付广告投放问题，这种方案能够有效地解决预测性不确定的用户行为、成千上万个合同和数十亿用户访问等问题。