  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.
