• Corpus ID: 20443433

An LSTM-Based Dynamic Customer Model for Fashion Recommendation

@inproceedings{Heinz2017AnLD,
  title={An LSTM-Based Dynamic Customer Model for Fashion Recommendation},
  author={Sebastian Heinz and Christian Bracher and Roland Vollgraf},
  booktitle={RecTemp@RecSys},
  year={2017}
}
Online fashion sales present a challenging use case for personalized recommendation: Stores offer a huge variety of items in multiple sizes. Small stocks, high return rates, seasonality, and changing trends cause continuous turnover of articles for sale on all time scales. Customers tend to shop rarely, but often buy multiple items at once. We report on backtest experiments with sales data of 100k frequent shoppers at Zalando, Europe's leading online fashion platform. To model changing customer… 

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