  Ad creatives are one of the prominent mediums for online e-commerce
advertisements. Ad creatives with enjoyable visual appearance may increase the
click-through rate (CTR) of products. Ad creatives are typically handcrafted by
advertisers and then delivered to the advertising platforms for advertisement.
In recent years, advertising platforms are capable of instantly compositing ad
creatives with arbitrarily designated elements of each ingredient, so
advertisers are only required to provide basic materials. While facilitating
the advertisers, a great number of potential ad creatives can be composited,
making it difficult to accurately estimate CTR for them given limited real-time
feedback. To this end, we propose an Adaptive and Efficient ad creative
Selection (AES) framework based on a tree structure. The tree structure on
compositing ingredients enables dynamic programming for efficient ad creative
selection on the basis of CTR. Due to limited feedback, the CTR estimator is
usually of high variance. Exploration techniques based on Thompson sampling are
widely used for reducing variances of the CTR estimator, alleviating feedback
sparsity. Based on the tree structure, Thompson sampling is adapted with
dynamic programming, leading to efficient exploration for potential ad
creatives with the largest CTR. We finally evaluate the proposed algorithm on
the synthetic dataset and the real-world dataset. The results show that our
approach can outperform competing baselines in terms of convergence rate and
overall CTR.
