skip to main content
10.1145/3534678.3542904acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
abstract

First Workshop on Content Understanding and Generation for E-commerce

Published:14 August 2022Publication History

ABSTRACT

Shopping experience on any e-commerce website is largely driven by the content customers interact with. The large volume of diverse content on e-commerce platforms, and the advances in machine learning, pose unique opportunities for gathering insights through content understanding and applying these insights to generate content better shopper experience. The purpose of the first edition of this workshop was to bring together researchers from industry and academia on questions surrounding e-commerce content understanding and generation.

References

  1. Kenan E Ak, Joo Hwee Lim, Jo Yew Tham, and Ashraf A Kassim. 2019. Attribute manipulation generative adversarial networks for fashion images. In ICCV.Google ScholarGoogle Scholar
  2. Federico Bianchi, Jacopo Tagliabue, and Bingqing Yu. 2021. Query2Prod2Vec: Grounded Word Embeddings for eCommerce. In NAACL. ACL. https://doi.org/10.18653/v1/2021.naacl-industry.20Google ScholarGoogle Scholar
  3. AICC CVPR. 2021. AI for Content Creation Workshop. https://visual.cs.brown. edu/workshops/aicc2021/ Accessed: 01/13/2022.Google ScholarGoogle Scholar
  4. CVFAD CVPR. 2021. Computer Vision for Fashion, Art, and Design Wokshop. https://sites.google.com/zalando.de/cvfad2021/home Accessed: 01/13/2022.Google ScholarGoogle Scholar
  5. Xintong Han, Zuxuan Wu, Weilin Huang, Matthew R Scott, and Larry S Davis. 2019. Finet: Compatible and diverse fashion image inpainting. In ICCV.Google ScholarGoogle Scholar
  6. KDD. 2020. InternationalWorkshop on Knowledge Graphs and E-Commerce. https://usc-isi-i2.github.io/KDD2020workshop/ Accessed: 02/23/2022.Google ScholarGoogle Scholar
  7. AdKDD KDD. 2021. InternationalWorkshop on Data Mining for Online Advertising. https://www.adkdd.org/ Accessed: 02/23/2022.Google ScholarGoogle Scholar
  8. AI for Fashion KDD. 2018. International Workshop on Fashion and KDD. https://kddfashion2018.mybluemix.net/ Accessed: 02/23/2022.Google ScholarGoogle Scholar
  9. IRS KDD. 2020. International Workshop on Industrial Recommendation Systems. https://irsworkshop.github.io/2020/index.html Accessed: 02/23/2022.Google ScholarGoogle Scholar
  10. Tae-Hyun Kim, Hye-Rin Kim, and Yeong-Jun Cho. 2021. Product Inspection Methodology via Deep Learning: An Overview. arXiv preprint arXiv:2103.16198 (2021).Google ScholarGoogle Scholar
  11. CtrlGen NIPS. 2021. Controllable Generative Modeling in Language and Vision Workshop. https://ctrlgenworkshop.github.io/ Accessed: 01/13/2022.Google ScholarGoogle Scholar
  12. Ronald van Bezu, Sjoerd Borst, Rick Rijkse, Jim Verhagen, Damir Vandic, and Flavius Frasincar. 2015. Multi-Component Similarity Method for Web Product Duplicate Detection. In SAC (SAC '15). ACM, 8. https://doi.org/10.1145/2695664.2695818Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Qifan Wang, Li Yang, Bhargav Kanagal, Sumit Sanghai, D. Sivakumar, Bin Shu, Zac Yu, and Jon Elsas. 2020. Learning to Extract Attribute Value from Product via Question Answering: A Multi-Task Approach. In KDD (KDD '20). ACM, 9. https://doi.org/10.1145/3394486.3403047Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Yaqing Wang, Yifan Ethan Xu, Xian Li, Xin Luna Dong, and Jing Gao. 2020. Automatic Validation of Textual Attribute Values in E-Commerce Catalog by Learning with Limited Labeled Data. In KDD (KDD '20). ACM. https://doi.org/10.1145/3394486.3403303Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. First Workshop on Content Understanding and Generation for E-commerce

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
        August 2022
        5033 pages
        ISBN:9781450393850
        DOI:10.1145/3534678

        Copyright © 2022 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 14 August 2022

        Check for updates

        Qualifiers

        • abstract

        Acceptance Rates

        Overall Acceptance Rate1,133of8,635submissions,13%

        Upcoming Conference

        KDD '24
      • Article Metrics

        • Downloads (Last 12 months)16
        • Downloads (Last 6 weeks)2

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader