Adversarial Factorization Autoencoder for Look-alike Modeling
Adversarial Factorization Autoencoder for Look-alike Modeling
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DOI：
10.1145/3357384.3357807
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Khoa D. Doan;Pranjul Yadav;Chandan K. Reddy
中科院分区：
文献类型：
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作者：
Khoa D. Doan;Pranjul Yadav;Chandan K. Reddy
数字广告以多种方式执行，例如，上下文、基于显示和基于搜索的广告。通过这些途径，广告主的主要目标是最大限度地提高投资回报。为了实现这一点，广告商通常旨在将广告瞄准目标受众集合，因为该集合具有对广告做出积极响应的高可能性。一种这样的定制和个性化的定向广告形式被称为外观相似建模，其中广告商提供一组种子用户，并期望机器学习模型识别新的一组用户，使得新识别的一组用户与在线购买活动的种子集相似。现有的相似建模技术（即，基于相似性的和基于回归的）由于在建模期间引入的隐含约束而受到严重的限制。此外，广告数据的高维和稀疏性质增加了复杂性。为了克服这些限制，在本文中，我们提出了一种新的对抗因子分解自动编码器，可以通过使用对抗训练过程有效地学习从稀疏高维数据到二进制地址空间的二进制映射。我们证明了我们所提出的方法对从现实世界中获得的数据集的有效性，并系统地比较了我们所提出的方法与现有的外观相似的建模基线的性能。
Digital advertising is performed in multiple ways, for e.g., contextual, display-based and search-based advertising. Across these avenues, the primary goal of the advertiser is to maximize the return on investment. To realize this, the advertiser often aims to target the advertisements towards a targeted set of audience as this set has a high likelihood to respond positively towards the advertisements. One such form of tailored and personalized, targeted advertising is known as look-alike modeling, where the advertiser provides a set of seed users and expects the machine learning model to identify a new set of users such that the newly identified set is similar to the seed-set with respect to the online purchasing activity. Existing look-alike modeling techniques (i.e., similarity-based and regression-based) suffer from serious limitations due to the implicit constraints induced during modeling. In addition, the high-dimensional and sparse nature of the advertising data increases the complexity. To overcome these limitations, in this paper, we propose a novel Adversarial Factorization Autoencoder that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure. We demonstrate the effectiveness of our proposed approach on a dataset obtained from a real-world setting and also systematically compare the performance of our proposed approach with existing look-alike modeling baselines.