一种基于多模态深度学习的房价预测方法
A Multi-Modal Deep Learning Based Approach for House Price Prediction
AI总结：
本文提出融合房屋属性、地理空间、文本描述与图像的多模态深度学习房价预测方法，通过联合嵌入和回归模型显著提升预测精度，并公开代码与数据集。
摘要：
房价的准确预测是住宅房地产领域的一个重要方面，受到各类利益相关者的广泛关注。然而，由于房屋特征、位置、社区等诸多因素造成的显著差异性，预测房价是一项复杂的任务。尽管已有大量研究尝试利用包括近期深度学习技术在内的各种算法来准确预测房价，但现有方法未能充分考虑文本和视觉特征等广泛因素。本文通过在房价预测系统中全面整合房地产挂牌信息中通常展示的属性（如房屋特征、文本描述、地理空间社区信息和房屋图像）来弥补这一空白。具体而言，我们提出了一种多模态深度学习方法，利用不同类型的数据学习更准确的房屋表示。特别地，我们学习原始房屋属性、地理空间社区信息，以及最重要的、来自代表房屋的文本描述和图像的联合嵌入；最后使用下游回归模型，根据这一联合学习得到的嵌入向量预测房价。我们在真实数据集上的实验结果表明，除原始属性和地理空间嵌入外，房屋广告描述的文本嵌入以及房屋图片的图像嵌入能够显著提高房价预测的准确性。相关源代码和数据集可通过以下URL公开获取：https://github.com/4P0N/mhpp
英文摘要：
Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
