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

• Published in: Knowledge and information systems 2020-12, Vol.62 (12), p.4541-4568
• Main Authors: Fathalla, Ahmed, Salah, Ahmad, Li, Kenli, Li, Keqin, Francesco, Piccialli
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Computer architecture; Computer Science; Data Mining and Knowledge Discovery; Database Management; Electronic commerce; Forecasting; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Machine learning; Mathematical models; Neural networks; Prices; Pricing; Regular Paper; Support vector machines; Websites
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-020-01495-8

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.