论文标题
多模式价格预测
Multimodal price prediction
论文作者
论文摘要
价格预测是与预测任务相关的示例之一，是一个基于数据科学的项目。价格预测分析数据并预测新产品的成本。这项研究的目的是实现根据手机规格预测价格的安排。因此，提出了五种深度学习模型，以预测手机，一种单峰和四种多模式方法的价格范围。多模式方法基于手机的图形和非图形特征预测价格，这些特征对其价值具有重要作用。此外，为了评估所提出方法的效率，已经从GSMARENA收集了手机数据集。实验结果显示88.3％的F1得分，这证实了多模式学习会导致比最新技术更准确的预测。
Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.
