  Advertising creatives are ubiquitous in E-commerce advertisements and
aesthetic creatives may improve the click-through rate (CTR) of the products.
Nowadays smart advertisement platforms provide the function of compositing
creatives based on source materials provided by advertisers. Since a great
number of creatives can be generated, it is difficult to accurately predict
their CTR given a limited amount of feedback. Factorization machine (FM), which
models inner product interaction between features, can be applied for the CTR
prediction of creatives. However, interactions between creative elements may be
more complex than the inner product, and the FM-estimated CTR may be of high
variance due to limited feedback. To address these two issues, we propose an
Automated Creative Optimization (AutoCO) framework to model complex interaction
between creative elements and to balance between exploration and exploitation.
Specifically, motivated by AutoML, we propose one-shot search algorithms for
searching effective interaction functions between elements. We then develop
stochastic variational inference to estimate the posterior distribution of
parameters based on the reparameterization trick, and apply Thompson Sampling
for efficiently exploring potentially better creatives. We evaluate the
proposed method with both a synthetic dataset and two public datasets. The
experimental results show our method can outperform competing baselines with
respect to cumulative regret. The online A/B test shows our method leads to a 7
increase in CTR compared to the baseline.
