Yahoo's native advertising (also known as Gemini native) serves billions of
ad impressions daily, reaching a yearly run-rate of many hundred of millions
USD. Driving the Gemini native models that are used to predict both click
probability (pCTR) and conversion probability (pCONV) is OFFSET - a feature
enhanced collaborative-filtering (CF) based event prediction algorithm. \offset
is a one-pass algorithm that updates its model for every new batch of logged
data using a stochastic gradient descent (SGD) based approach. Since OFFSET
represents its users by their features (i.e., user-less model) due to sparsity
issues, rule based hard frequency capping (HFC) is used to control the number
of times a certain user views a certain ad. Moreover, related statistics reveal
that user ad fatigue results in a dramatic drop in click through rate (CTR).
Therefore, to improve click prediction accuracy, we propose a soft frequency
capping (SFC) approach, where the frequency feature is incorporated into the
OFFSET model as a user-ad feature and its weight vector is learned via logistic
regression as part of OFFSET training. Online evaluation of the soft frequency
capping algorithm via bucket testing showed a significant 7.3% revenue lift.
Since then, the frequency feature enhanced model has been pushed to production
serving all traffic, and is generating a hefty revenue lift for Yahoo Gemini
native. We also report related statistics that reveal, among other things, that
while users' gender does not affect ad fatigue, the latter seems to increase
with users' age.

通过引入频次约束特征，并结合逻辑回归的 OFFSET 模型训练，我们提出了一种软频次约束方法，通过桶测试在线评估，结果显示收入大幅提升了 7.3％。此后，经过推广并应用于所有流量，该增强模型为 Yahoo Gemini native 带来了可观的收入提升。

雅虎 Native 平台中用于改善广告点击预测的软频率上限

Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini  Native

Click prediction is one of the fundamental problems in sponsored search. Most
of existing studies took advantage of machine learning approaches to predict ad
click for each event of ad view independently. However, as observed in the
real-world sponsored search system, user's behaviors on ads yield high
dependency on how the user behaved along with the past time, especially in
terms of what queries she submitted, what ads she clicked or ignored, and how
long she spent on the landing pages of clicked ads, etc. Inspired by these
observations, we introduce a novel framework based on Recurrent Neural Networks
(RNN). Compared to traditional methods, this framework directly models the
dependency on user's sequential behaviors into the click prediction process
through the recurrent structure in RNN. Large scale evaluations on the
click-through logs from a commercial search engine demonstrate that our
approach can significantly improve the click prediction accuracy, compared to
sequence-independent approaches.

通过引入循环神经网络 (RNN) 的回归结构，直接建模用户序列行为依赖，从而提高点击预测准确性。

使用循环神经网络进行赞助搜索的顺序点击预测

Sequential Click Prediction for Sponsored Search with Recurrent Neural  Networks

Online sequence prediction is the problem of predicting the next element of a
sequence given previous elements. This problem has been extensively studied in
the context of individual sequence prediction, where no prior assumptions are
made on the origin of the sequence. Individual sequence prediction algorithms
work quite well for long sequences, where the algorithm has enough time to
learn the temporal structure of the sequence. However, they might give poor
predictions for short sequences. A possible remedy is to rely on the general
model of prediction with expert advice, where the learner has access to a set
of $r$ experts, each of which makes its own predictions on the sequence. It is
well known that it is possible to predict almost as well as the best expert if
the sequence length is order of $\log(r)$. But, without firm prior knowledge on
the problem, it is not clear how to choose a small set of {\em good} experts.
In this paper we describe and analyze a new algorithm that learns a good set of
experts using a training set of previously observed sequences. We demonstrate
the merits of our approach by applying it on the task of click prediction on
the web.

本文介绍一种在线学习算法，利用先前观察到的数据训练一组有效的专家，以在短序列上进行点击预测。