更便宜更好：电子商务推荐系统中用于转化率预测的折扣感知网络
Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System
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中文总结 AI 辅助
电商推荐系统中点击后转化率预测面临诸多挑战，本文提出折扣感知网络DANet，通过时频变换、分布去偏和监督回归辅助任务建模商品折扣率与CVR关系，实验证明其能提升预测性能，已成功部署。
中文摘要 AI 辅助
点击后转化率（CVR）是在线推荐系统中的关键要素，面临数据稀疏、样本选择偏差和延迟反馈等挑战。然而，商品折扣率这一影响定价和用户购买行为的关键因素却未受足够关注。本文引入折扣感知网络（DANet）来建模商品折扣率与CVR的关系。DANet包含三个主要组件：利用傅里叶变换获取频谱并捕捉商品长期折扣率趋势的时频变换模块；减轻因购买组合和促销活动导致的用户特定折扣率偏差以及电商平台不同促销期相关周期性偏差的分布去偏模块；建立明确商品折扣标签以提高模型价值准确性表现的监督回归辅助任务。真实数据集实验结果表明DANet的优越性，离线AUC提高1.61%，在线A/B测试中pCVR提高3.63%，GMV提高2.23%。DANet已成功部署在阿里巴巴天猫APP上。
英文摘要
Post-click conversion rate (CVR) is a crucial element in online recommendation systems, which addresses significant challenges such as data sparsity (DS), sample selection bias (SSB), and delayed feedback. However, the impact of item discount rate-a key factor influencing both pricing and user purchasing behavior, has received limited attention. In this paper, we introduce the Discount-Aware Network (DANet) to model the relationship between item discount rates and CVR. DANet comprises three main components: 1) a time-frequency transformation module that utilizes Fourier transform to derive the frequency spectrum and capture the long-term discount rate trends of items; 2) a distribution de-bias module designed to mitigate the biases in user-specific discount rates caused by various purchase combinations and promotional activities, as well as periodic deviations linked to different promotion periods on e-commerce platforms; and 3) a supervised regression auxiliary task that establishes the explicit item discount labels to enhance the model's performance in terms of value accuracy, facilitating an effective representation of item discount rates. Experimental results on real datasets demonstrate the superiority of DANet, with offline AUC improving by 1.61%, and online A/B test also shows that DANet achieves impressive gains of 3.63% on pCVR and 2.23% on GMV. DANet has been successfully deployed on Alibaba Tmall APP. The code is available at https://github.com/tangrc/DANet.
