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New survival models enhance e-commerce repurchase prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new approach to predicting customer repurchase behavior in e-commerce using survival models, which directly estimate the time until a repurchase occurs. This method replaces multiple binary classifiers for different time horizons with a single model, showing improved efficiency and performance. The study also identified a trade-off between calibration quality and ranking performance within the Accelerated Failure Time (AFT) model family, suggesting different models for different applications. AI

IMPACT This research could lead to more accurate and efficient recommendation systems in e-commerce.

RANK_REASON Academic paper detailing a new methodology for prediction models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New survival models enhance e-commerce repurchase prediction

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Academic paper detailing a new methodology for prediction models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Akshay Kekuda, Shreeranjani Srirangamsridharan, Ishan Bhatt, Yanan Cao, Sinduja Subramaniam, Evren Korpeoglu, Kaushiki Nag, Kannan Achan ·

    Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation

    arXiv:2608.28393v1 Announce Type: cross Abstract: Repurchase recommenders in e-commerce are commonly framed as a binary question asking "will this customer buy this item within W days", a formulation that requires a separately trained model for every horizon of interest. We repla…

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