基于树结构的组合广告创意的有效优选
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
摘要：
广告创意是在线电子商务广告的主要媒介之一。具有令人愉快的视觉外观的广告创意可以增加产品的点击率（CTR）。广告创意通常由广告商手工制作，然后交付给广告平台用于广告。近年来，广告平台能够即时合成具有任意指定的每种成分的元素的广告创意，因此广告主只需要提供基本材料。在方便广告商的同时，可以合成大量潜在的广告创意，使得在给定有限的实时反馈的情况下难以准确地估计针对它们的CTR。为此，我们提出了一种基于树结构的自适应和高效的广告创意选择（AES）框架。合成成分的树结构使动态规划能够在CTR的基础上进行有效的广告创意选择。由于有限的反馈，CTR估计器通常具有高方差。基于Thompson抽样的探索技术被广泛用于减小CTR估计器的方差，减轻反馈稀疏性。在树结构的基础上，采用动态规划对Thompson抽样进行调整，从而有效地挖掘具有最大点击率的潜在广告创意。最后，我们评估所提出的算法在合成数据集和真实世界的数据集。结果表明，我们的方法可以优于竞争基线的收敛速度和整体CTR。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
