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        <title>Learning Reasons for Product Returns on E-Commerce</title>
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    <name type="personal">
        <namePart type="given">Miriam</namePart>
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            <title>Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024</title>
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        <name type="personal">
            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
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            <namePart type="given">Oleg</namePart>
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        <name type="personal">
            <namePart type="given">Eugene</namePart>
            <namePart type="family">Agichtein</namePart>
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    <abstract>In the rapidly evolving landscape of e-commerce, product returns have become a significant economic burden for businesses, where the reasons for returns may vary from wrong sizing and defective products to simply no longer needing the purchased product. This paper presents, to the best of our knowledge, the first comprehensive study of the complexities of product returns across a variety of e-commerce domains, focusing on the task of predicting the return reason. We propose a supervised approach for predicting return likelihood and the underlying return reason. We test our approach over a real-world dataset from a large e-commerce platform.</abstract>
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%0 Conference Proceedings
%T Learning Reasons for Product Returns on E-Commerce
%A Farber, Miriam
%A Novgorodov, Slava
%A Guy, Ido
%Y Malmasi, Shervin
%Y Fetahu, Besnik
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F farber-etal-2024-learning
%X In the rapidly evolving landscape of e-commerce, product returns have become a significant economic burden for businesses, where the reasons for returns may vary from wrong sizing and defective products to simply no longer needing the purchased product. This paper presents, to the best of our knowledge, the first comprehensive study of the complexities of product returns across a variety of e-commerce domains, focusing on the task of predicting the return reason. We propose a supervised approach for predicting return likelihood and the underlying return reason. We test our approach over a real-world dataset from a large e-commerce platform.
%U https://aclanthology.org/2024.ecnlp-1.1
%P 1-7

Markdown (Informal)

[Learning Reasons for Product Returns on E-Commerce](https://aclanthology.org/2024.ecnlp-1.1) (Farber et al., ECNLP-WS 2024)

• Learning Reasons for Product Returns on E-Commerce (Farber et al., ECNLP-WS 2024)

ACL

• Miriam Farber, Slava Novgorodov, and Ido Guy. 2024. Learning Reasons for Product Returns on E-Commerce. In Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024, pages 1–7, Torino, Italia. ELRA and ICCL.