A Latent Factor-Based Bayesian Neural Networks Model in Cloud Platform for Used Car Price Prediction
提出一种在云平台上运行的贝叶斯神经网络模型，通过提取潜在因子去除数据噪声，并用概率分布表示权重以缓解过拟合，在真实数据集上预测二手车价格，效果优于现有基准模型。
The selling price of a used car can be predicted based on its historical information. Accurate and reasonable used car price evaluation will be able to promote the healthy progress of the used car industry. Current used car price prediction models are troubled by data quality and the inability to provide estimates of uncertain information, and the prediction accuracy cannot meet the needs of real-world scenarios. In this work, we propose a price prediction model based on a Bayesian neural networks with latent factor (LFBNN) in a cloud platform, which is capable of performing latent factor extraction operations on structured data of used cars as a way to remove the effect of noise in the dataset on the model performance. Moreover, the weight parameters of the Bayesian neural networks (NNs) are represented as probability distributions, which is equivalent to introducing uncertainty and acting as a regularization effect compared to a NN with fixed weights, thus making it possible to alleviate the overfitting problem. The LFBNN model uses a cloud platform for data transmission and storage, enabling it to run faster. Compared with the current benchmark models, the model proposed in this work achieves excellent experimental results on two real datasets. The experimental results on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Car_{1}$</tex-math></inline-formula> dataset are 1.498 for mse, 1.224 for rmse, and 0.995 for MAE, and 1.519 for mse, 1.232 for rmse, and 1.002 for MAE on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Car_{2}$</tex-math></inline-formula> dataset.