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- W4308790790 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." @default.
- W4308790790 created "2022-11-15" @default.
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- W4308790790 date "2022-11-11" @default.
- W4308790790 modified "2023-09-26" @default.
- W4308790790 title "Sequence aware recommenders for fashion E-commerce" @default.
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- W4308790790 doi "https://doi.org/10.1007/s10660-022-09627-8" @default.
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