PreSizE: Predicting Size in E-Commerce using Transformers

@article{Eshel2021PreSizEPS,
  title={PreSizE: Predicting Size in E-Commerce using Transformers},
  author={Yotam Eshel and Or Levi and Haggai Roitman and Alexander Nus},
  journal={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2021}
}
  • Yotam EshelOr Levi A. Nus
  • Published 4 May 2021
  • Computer Science, Business
  • Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Recent advances in the e-commerce fashion industry have led to an exploration of novel ways to enhance buyer experience via improved personalization. Predicting a proper size for an item to recommend is an important personalization challenge, and is being studied in this work. Earlier works in this field either focused on modeling explicit buyer fitment feedback or modeling of only a single aspect of the problem (e.g., specific category, brand, etc.). More recent works proposed richer models… 

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