  Click-through rate (CTR) prediction has been one of the most central problems
in computational advertising. Lately, embedding techniques that produce
low-dimensional representations of ad IDs drastically improve CTR prediction
accuracies. However, such learning techniques are data demanding and work
poorly on new ads with little logging data, which is known as the cold-start
problem.
  In this paper, we aim to improve CTR predictions during both the cold-start
phase and the warm-up phase when a new ad is added to the candidate pool. We
propose Meta-Embedding, a meta-learning-based approach that learns to generate
desirable initial embeddings for new ad IDs. The proposed method trains an
embedding generator for new ad IDs by making use of previously learned ads
through gradient-based meta-learning. In other words, our method learns how to
learn better embeddings. When a new ad comes, the trained generator initializes
the embedding of its ID by feeding its contents and attributes. Next, the
generated embedding can speed up the model fitting during the warm-up phase
when a few labeled examples are available, compared to the existing
initialization methods.
  Experimental results on three real-world datasets showed that Meta-Embedding
can significantly improve both the cold-start and warm-up performances for six
existing CTR prediction models, ranging from lightweight models such as
Factorization Machines to complicated deep models such as PNN and DeepFM. All
of the above apply to conversion rate (CVR) predictions as well.
