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.

近年来见证了在线购物和电子商务网站（例如eBay和OLX）的快速发展。在线购物市场每天提供数百万种待售产品。这些产品分为许多产品类别。对于卖家而言，正确估算二手商品的价格至关重要。最先进的方法只能预测一种商品的价格。此外，在现有的方法中，没有一种方法可以在预测任务中利用给定二手商品的价格范围，因为同一产品有多个价格不同的广告。因此，作为第一个贡献，我们提出了一种深层的模型体系结构，该模型用于基于不同类型的项目类型的项目的图像和文本描述来预测二手项目的价格。该提议的方法利用涉及长短期记忆（LSTM）的深度神经网络和卷积神经网络体系结构进行价格预测。与支持向量机基线模型相比，所提出的模型获得了更好的平均绝对误差准确度评分。另外，第二贡献包括双重。首先，我们建议预测二手商品的最低和最高价格。用于预测任务的模型利用线性回归，LSTM和季节性自回归综合移动平均值方法。其次，我们建议利用第一贡献模型来预测项目质量得分。然后，将商品质量得分与预测的最低和最高价格相结合，以提供商品的最终预测价格。