@inproceedings{farber-etal-2024-learning,
    title = "Learning Reasons for Product Returns on {E}-Commerce",
    author = "Farber, Miriam  and
      Novgorodov, Slava  and
      Guy, Ido",
    editor = "Malmasi, Shervin  and
      Fetahu, Besnik  and
      Ueffing, Nicola  and
      Rokhlenko, Oleg  and
      Agichtein, Eugene  and
      Guy, Ido",
    booktitle = "Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.ecnlp-1.1",
    pages = "1--7",
    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.",
}

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.
Farber, Miriam Novgorodov, Slava Guy, Ido
Learning Reasons for Product Returns on E-Commerce