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New DANet model improves e-commerce conversion rates by analyzing discount trends

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new model called DANet to improve conversion rate prediction in e-commerce recommendation systems. DANet specifically addresses the impact of item discount rates, a factor often overlooked in existing models. The system utilizes a time-frequency transformation module with Fourier transforms to analyze discount trends, a distribution de-bias module to correct for biases in user-specific discount data, and a supervised regression task to enhance discount rate representation. Successfully deployed on the Alibaba Tmall APP, DANet has shown significant improvements in both offline AUC and online pCVR and GMV metrics. AI

IMPACT Enhances e-commerce recommendation systems by optimizing conversion rate prediction through discount analysis.

RANK_REASON The cluster contains a research paper detailing a new model and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New DANet model improves e-commerce conversion rates by analyzing discount trends

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The cluster contains a research paper detailing a new model and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jing Wang ·

    Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System

    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 pr…

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