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Deep end-to-end learning for price prediction of second-hand items

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Abstract

Recent years have witnessed the rapid development of online shopping and ecommerce websites, e.g., eBay and OLX. Online shopping markets offer millions of products for sale each day. These products are categorized into many product categories. It is crucial for sellers to correctly estimate the price of the second-hand item. State-of-the-art methods can predict the price of only one item category. In addition, none of the existing methods utilized the price range of a given second-hand item in the prediction task, as there are several advertisements for the same product at different prices. In this vein, as the first contribution, we propose a deep model architecture for predicting the price of a second-hand item based on the image and textual description of the item for different sets of item types. This proposed method utilizes a deep neural network involving long short-term memory (LSTM) and convolutional neural network architectures for price prediction. The proposed model achieved a better mean absolute error accuracy score in comparison with the support vector machine baseline model. In addition, the second contribution includes twofold. First, we propose forecasting the minimum and maximum prices of the second-hand item. The models used for the forecasting task utilize linear regression, LSTM, and seasonal autoregressive integrated moving average methods. Second, we propose utilizing the model of the first contribution in predicting the item quality score. Then, the item quality score and the forecasted minimum and maximum prices are combined to provide the item’s final predicted price. Using a dataset crawled from a website for second-hand items, the proposed method of combining the predicted second-hand item quality score with the forecasted minimum and maximum price outperforms the other models in all of the used accuracy metrics with a significant performance gap.

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Notes

  1. https://fred.stlouisfed.org/series/IQ41000

  2. https://www.seleniumhq.org/docs/03_webdriver.jsp

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Acknowledgements

This research is partly supported by the National Key R&D Program of China (Grants: SQ2018YFB020061) and the NSFC (61860206011, 61625202, 61602170, and 61750110531).

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Author notes

  1. Ahmed Fathalla, Ahmad Salah and Kenli Li have contributed equally to this work

Authors and Affiliations

  1. College of Computer Science and Electrical Engineering, Hunan University, and the National Supercomputing Center in Changsha, Changsha, Hunan, China

    Ahmed Fathalla, Ahmad Salah, Kenli Li & Keqin Li

  2. Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt

    Ahmed Fathalla

  3. Faculty of Computers and Informatics, Zagazig University, Sharkia, Egypt

    Ahmad Salah

  4. State University of New York, New York, USA

    Keqin Li

  5. University of Naples Federico II, Naples, Italy

    Piccialli Francesco

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  1. Ahmed Fathalla
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Correspondence to Ahmed Fathalla or Kenli Li.

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Fathalla, A., Salah, A., Li, K. et al. Deep end-to-end learning for price prediction of second-hand items. Knowl Inf Syst 62, 4541–4568 (2020). https://doi.org/10.1007/s10115-020-01495-8

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