摘要
随着民宿与在线短租平台的兴起,房东多归属现象持续受到关注与研究,该现象提供了新的研究角度,而如何在不同平台识别同源房东成为首要解决的问题。故本文基于传统用户匹配探索C2C在线短租跨平台房东匹配算法。其中由于房东个人信息稀疏,因此本文引入房源信息,设计基于房源信息的两阶段房东匹配算法(TSHM)。本文方法在基于国内2大在线短租平台真实数据划分的普通数据集与难例数据集上分别达到99.69%与81.97%的准确率,优于SVM、DT等传统分类器,验证了匹配模型与匹配特征的有效性,为跨平台房东匹配提供新思路,在房东个人信息缺乏条件下仍可有效匹配房东。但本文仅针对国内平台数据进行实验,未引入文本与图片等特征,存在一定局限性。
With the rise of homestays and online short-term rental platforms,the phenomenon of host multiple ownership continues to receive attention and research.This phenomenon provides a new research perspective,and how to identify same-source hosts on different platforms has become the first problem to be solved.Therefore,this article explores the C2C online short-term rental cross-platform host matching algorithm based on traditional user matching.Among them,due to the sparse personal information of the host,this paper introduces housing information and designs a two-stage host matching algorithm( TSHM) based on housing.The method in this paper achieves 99.69% and 81.97% accuracy on the common data set and the hard-case data set based on the real data of the two domestic online short-term rental platforms,respectively,which is better than traditional classifiers such as SVM and DT.The matching model is verified.The effectiveness of the matching features provides a new idea for cross-platform host matching,which can still effectively match the host even if the host’s personal information is lacking.However,this article only conducts experiments on domestic platform data,and does not introduce features such as text and pictures,which has certain limitations.
作者
吴代漾
赵洁
梁家铭
董振宁
梁周扬
WU Dai-yang;ZHAO Jie;LIANG Jia-ming;DONG Zhen-ning;LIANG Zhou-yang(School of Management,Guangdong University of Technology,Guangzhou 510520,China)
出处
《计算机与现代化》
2022年第6期43-48,79,共7页
Computer and Modernization
基金
国家自然科学基金资助项目(71871069)
教育部人文社会科学研究规划项目(18YJAZH137)。
关键词
在线短租
房东匹配
遗传算法
多归属
online short-term rent
host matching
genetic algorithm
multi-homing
