Airbnb价格预测的多源信息学习框架

Lu Jiang, Y. Li, Na Luo, Jianan Wang, Qiao Ning
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引用次数: 0

摘要

随着科技和共享经济的发展,Airbnb作为著名的短租平台,已经成为很多年轻人选择的首选。Airbnb的定价问题一直是一个值得研究的问题。虽然以往的研究取得了可喜的成果,但也存在不足。如:(1)租赁的特征属性不够丰富;(2)租赁文本信息研究不够深入;(3)结合房屋周边兴趣点(POI)预测租金价格的研究较少。为了解决上述挑战,我们提出了一个多源信息嵌入(MSIE)模型来预测Airbnb的租金价格。具体来说,我们首先选择统计特征嵌入原始租赁数据。其次,生成三种不同文本信息的词特征向量和情感得分组合,形成文本特征嵌入;第三,我们利用兴趣点(POI)周围的出租房屋信息生成各种空间网络图,并学习网络的嵌入来获得空间特征嵌入。最后,本文将三个模块组合成多源租金表示,并使用构建的全连接神经网络进行价格预测。实验结果表明了该模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Source Information Learning Framework for Airbnb Price Prediction
With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.
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