Sequence aware recommenders for fashion E-commerce

Yang Sok Kim, Hyunwoo Hwangbo (), Hee Jun Lee and Won Seok Lee
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Yang Sok Kim: Keimyung University
Hyunwoo Hwangbo: Hana Financial Group, Chief Data Officer
Hee Jun Lee: Keimyung University
Won Seok Lee: Keimyung University

Electronic Commerce Research, 2024, vol. 24, issue 4, No 20, 2733-2753

Abstract: 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.

Keywords: Recommendation system; Sequence-aware recommendation; Deep learning; Fashion E-commerce; Fashion product recommendation; Recurrent neural network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10660-022-09627-8

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