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- W2788493241 abstract "Lack of calibrated product sizing in popular categories such as apparel and shoes leads to customers purchasing incorrect sizes, which in turn results in high return rates due to fit issues. We address the problem of product size recommendations based on customer purchase and return data. We propose a novel approach based on Bayesian logit and probit regression models with ordinal categories Small, Fit, Largeto model size fits as a function of the difference between latent sizes of customers and products. We propose posterior computation based on mean-field variational inference, leveraging the Polya-Gamma augmentation for the logit prior, that results in simple updates, enabling our technique to efficiently handle large datasets. Our Bayesian approach effectively deals with issues arising from noise and sparsity in the data providing robust recommendations. Offline experiments with real-life shoe datasets show that our model outperforms the state-of-the-art in 5 of 6 datasets. and leads to an improvement of 17-26% in AUC over baselines when predicting size fit outcomes." @default.
- W2788493241 created "2018-03-06" @default.
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- W2788493241 date "2018-01-01" @default.
- W2788493241 modified "2023-10-14" @default.
- W2788493241 title "Bayesian Models for Product Size Recommendations" @default.
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- W2788493241 doi "https://doi.org/10.1145/3178876.3186149" @default.
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