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Sequence aware recommenders for fashion E-commerce

Author

Listed:
  • Yang Sok Kim

    (Keimyung University)

  • Hyunwoo Hwangbo

    (Hana Financial Group, Chief Data Officer)

  • Hee Jun Lee

    (Keimyung University)

  • Won Seok Lee

    (Keimyung University)

Abstract

In recent years, fashion e-commerce has become more and more popular. Since there are so many fashion products provided by e-commerce retailers, it is necessary to provide recommendation services to users to minimize information overload. When users look for a product on an e-commerce website, they usually click the product information sequentially. Previous recommenders, such as content-based recommenders and collaborative filtering recommenders, do not consider this important behavioral characteristic. To take advantage of this important characteristic, this study proposes sequence-aware recommenders for fashion product recommendation using a gated recurrent unit (GRU) algorithm. We conducted an experiment using a dataset collected from an e-commerce website of a Korean fashion company. Experimental results show that sequence aware recommenders outperform non-sequence aware recommender, and multiple sequence-based recommenders outperform a single sequence-based recommender because they consider the attributes of fashion products. Finally, we discuss the implications of our study on fashion recommendations and propose further research topics.

Suggested Citation

  • Yang Sok Kim & Hyunwoo Hwangbo & Hee Jun Lee & Won Seok Lee, 2024. "Sequence aware recommenders for fashion E-commerce," Electronic Commerce Research, Springer, vol. 24(4), pages 2733-2753, December.
    • Handle: RePEc:spr:elcore:v:24:y:2024:i:4:d:10.1007_s10660-022-09627-8
    DOI: 10.1007/s10660-022-09627-8
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    References listed on IDEAS

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    1. Hyunwoo Hwangbo & Yangsok Kim, 2019. "Session-Based Recommender System for Sustainable Digital Marketing," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
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