Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System
For e-commerce recommendation systems, this work provides a novel method to incorporate discount information, yielding measurable improvements in conversion rate and revenue.
The paper tackles the problem of conversion rate prediction in e-commerce, specifically addressing the overlooked impact of item discount rates. The proposed Discount-Aware Network (DANet) achieves offline AUC improvement of 1.61% and online gains of 3.63% on pCVR and 2.23% on GMV on Alibaba Tmall.
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.