Multi-task Conditional Attention Network for Conversion Prediction in Logistics Advertising
Authors: 
Baoshen Guo, Xining Song, Shuai Wang, Wei Gong, Tian He, Xue LiuAuthors Info & Claims
Baoshen Guo, Xining Song, Shuai Wang, Wei Gong, Tian He, Xue LiuAuthors Info & ClaimsPages 5028 - 5037
Abstract
Logistics advertising is an emerging task in online-to-offline logistics systems, where logistics companies expand parcel shipping services to new users through advertisements on shopping websites. Compared to existing online e-commerce advertising, logistics advertising has two significant new characteristics: (i) the complex factors in logistics advertising considering both users' offline logistics preference and online purchasing profiles; and (ii) data sparsity and mutual relations among multiple steps due to longer advertising conversion processes. To address these challenges, we design MCAC, a Multi-task Conditional Attention network-based logistics advertising Conversion prediction framework, which consists of (i) an offline shipping preference extraction model to extract the user's offline logistics preference from historical shipping records, and (ii) a multi-task conditional attention-based conversion rate prediction module to model mutual relations among multiple steps in logistics advertising conversion processes. We evaluate and deploy MCAC on one of the largest e-commerce platforms in China for logistics advertising. Extensive offline experiments show that our method outperforms state-of-the-art baselines in various metrics. Moreover, the conversion rate prediction results of large-scale online A/B testing show that MCAC achieves a 15.22% improvement compared to existing industrial practices, which demonstrates the effectiveness of the proposed framework.
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