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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (2): 129-140    DOI: 10.11925/infotech.2096-3467.2020.0690
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Framework for Computing Trust in Online Short-Rent Platform Using Feature Selection of Images and Texts
Liang Jiaming,Zhao Jie(),Zheng Peng,Huang Liushen,Ye Minqi,Dong Zhenning
School of Management, Guangdong University of Technology, Guangzhou 510520, China
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Abstract  

[Objective] This paper proposes a novel framework to compute consumer trust of online short-rent platform. It provides multiple groups of low-dimension feature subsets for users to present their personal information, which addresses the issues of missing data. [Methods] We used rough-set feature selection based on evolutionary algorithm to extract information from images and texts. [Results] The proposed framework reduced dimension to 5% of the original feature set while classification accuracy remained unchanged. [Limitations] More research is needed to examine our model with data from overseas platforms. [Conclusions] The proposed framework could effectively compute users’ trust while protecting their privacy.

Key wordsOnline Short-Rent      Trust Computing      Feature Selection     
Received: 15 July 2020      Published: 11 March 2021
ZTFLH:  G203  
Fund:National Natural Science Foundation of China(71871069);Humanities and Social Sciences Planning Fund of the Ministry of Education(18YJAZH137);13th Five-Year Plan Project of Philosophy and Social Sciences Research of Guangdong Province, China(2018GZGJ48)
Corresponding Authors: Zhao Jie ORCID:0000-0003-3315-4447     E-mail: zhaojie@gdut.edu.cn

Cite this article:

Liang Jiaming, Zhao Jie, Zheng Peng, Huang Liushen, Ye Minqi, Dong Zhenning. Framework for Computing Trust in Online Short-Rent Platform Using Feature Selection of Images and Texts. Data Analysis and Knowledge Discovery, 2021, 5(2): 129-140.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0690     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I2/129

思路 特征提取 特征权重分析 优点 不足
基于理论提取特征 基于心理学 结构方程:文献[5,12]等 特征提取具有较好现实解释性 特征抽象,无法客观量化;研究结论无法适应网站动态变化
基于行为学 结构方程:文献[31]等
其他统计学方法:文献[13,32]等
基于社会认知学 结构方程:文献[14]等
基于数据提取特征 基于结构化数据 回归分析:文献[16,18-19]等 特征提取、数据分析难度低 只关注数值特征对交易的影响,
未结合非结构化数据刻画的对象
基于非结构化数据 回归分析:文献[23]等
其他方法:文献[21,25]等
文本、图像提供更丰富特征描述在线短租平台实情 只关注文本或图像对信任的影响,未综合考虑多源数据全面分析
基于结构化、非结构化数据 回归分析:文献[22,29]等 综合考虑多源数据更全面描述在线短租平台交易,特征丰富 特征提取涉及多源数据分析,特征维度较高,分析难度较高
Summary of Feature Extraction and Analysis on Online Short-Rent Platform Researches
29] and the Proposed One
">
Existing Computational Framework[29] and the Proposed One
量化使用变量 公式(1)[29] 改进公式(2) 改进说明
修正评论数 #Reviews(li) #Reviews(li) -
房东n~k月评论数 Reviewsn,k(li) Reviewsn,k(li) -
修正系数 a a -
房东n~k月房屋可租住天数 OnlineDaysn, k(li) TradeTimen,k(li) 未获取租住天数数据,公式(2)使用历史交易时间差代替
房东房屋数 - Houses(li) 公式(1)假设房东与房屋为一对一关系,公式(2)综合考虑房东与房屋可能为一对一或一对多关系
Perceived Trust Calculation
信任计算模型 分类器 特征选择 精度说明 2级 3级 5级
维度 精度 维度 精度 维度 精度
模型1 SVM 原始值 420 97.31% 420 95.18% 420 92.94%
模型2 SVM 均值 17 96.62% 23 93.98% 24 91.52%
中值 17 96.62% 23 94.20% 24 91.78%
模型3 DNN 原始值 420 97.58% 420 95.24% 420 93.35%
模型4 DNN 均值 17 97.83% 23 95.00% 24 92.74%
中值 17 97.87% 23 96.13% 24 94.10%
Results of Landlord Perceived Trust Prediction Accuracy
ROC Curves of Landlord Perceived Trust Prediction
ROC Curves of Landlord Perceived Trust Prediction
Dimensions in Landlord Perceived Trust Predictions
Dimensions in Landlord Perceived Trust Predictions
可信级别 2017.1-2017.12 2017.4-2018.3 2017.7-2018.6 2017.10-2018.9 2018.1-2018.12 均值
2级 696(17.6) 731(17.3) 775(17.1) 741(17.5) 632(17.6) 715(17)
3级 279(23.1) 241(23.3) 325(23.6) 347(22.7) 294(23.5) 297(23)
5级 295(25.0) 238(24.6) 219(23.5) 333(24.2) 239(24.1) 265(24)
Feature Subset Numbers and Feature Lengths
可信级别 2017.1-2017.12 2017.4-2018.3 2017.7-2018.6 2017.10-2018.9 2018.1-2018.12 均值
2级 696(17.6) 731(17.3) 775(17.1) 741(17.5) 632(17.6) 715(17)
3级 279(23.1) 241(23.3) 325(23.6) 347(22.7) 294(23.5) 297(23)
5级 295(25.0) 238(24.6) 219(23.5) 333(24.2) 239(24.1) 265(24)
Feature Subset Numbers and Feature Lengths
Feature Subset Numbers and Feature Lengths
Feature Subset Numbers and Feature Lengths
类别 特征
房东印象 1. 房东性别;2. 房东头像人脸微笑程度均值
租住质量 3. 标记“实拍”房屋比例;4. 标记“速订”比例;5. 宜居人数最大值;6. 含宽度为1.5~1.8m规格床房屋比例;7. 含宽度大于1.8m规格床房屋比例
交易细节 8. 最小入住天数最大值;9. 接待境外人士房屋比例;10. 允许加客房屋比例
历史租客信息 11. 租客注册时间最小值(年);12. 租客注册时间均值(月);13. 租客首次租房时间最大值(月);14. 租客最近租房时间均值(月)
评论信息 15. 评论总数;16. 评论短句粒度下“负向”文本情感占比
An Example of Feature Subset
类别 特征
房东印象 1. 房东性别;2. 房东头像人脸微笑程度均值
租住质量 3. 标记“实拍”房屋比例;4. 标记“速订”比例;5. 宜居人数最大值;6. 含宽度为1.5~1.8m规格床房屋比例;7. 含宽度大于1.8m规格床房屋比例
交易细节 8. 最小入住天数最大值;9. 接待境外人士房屋比例;10. 允许加客房屋比例
历史租客信息 11. 租客注册时间最小值(年);12. 租客注册时间均值(月);13. 租客首次租房时间最大值(月);14. 租客最近租房时间均值(月)
评论信息 15. 评论总数;16. 评论短句粒度下“负向”文本情感占比
An Example of Feature Subset
排名 2级 3级 5级
特征 频率 特征 频率 特征 频率
1 评论数 100% 租客注册时间最大值(月) 99% 租客最近租房时间最小值(月) 100%
2 租客首次租房时间最大值(月) 81% 租客注册时间均值(月) 94% 租客最近租房时间均值(月) 100%
3 租客注册时间最大值(月) 67% 租客首次租房时间最大值(月) 92% 租客注册时间最小值(年) 100%
4 接待境外人士房屋比例 59% 配备智能门锁房屋比例 89% 租客首次租房时间最大值(月) 99%
5 配备智能门锁房屋比例 59% 评论数 87% 租客注册时间最大值(月) 99%
6 租客最近租房时间均值(月) 58% 租客注册时间最小值(年) 86% 配备智能门锁房屋比例 91%
7 金牛座租客个数 52% 接待境外人士房屋比例 79% 评论数 90%
8 租客注册时间均值(月) 51% 房屋先住后付比例 76% 租客注册时间均值(月) 89%
9 房屋标记“优品”比例 48% 射手座租客个数 75% 房东昵称是否含字母 88%
10 房东性别 48% 房东昵称是否含字母 73% 含宽度大于1.8m规格床房屋比例 77%
Top 10 Most Frequent Features in Feature Subsets
排名 2级 3级 5级
特征 频率 特征 频率 特征 频率
1 评论数 100% 租客注册时间最大值(月) 99% 租客最近租房时间最小值(月) 100%
2 租客首次租房时间最大值(月) 81% 租客注册时间均值(月) 94% 租客最近租房时间均值(月) 100%
3 租客注册时间最大值(月) 67% 租客首次租房时间最大值(月) 92% 租客注册时间最小值(年) 100%
4 接待境外人士房屋比例 59% 配备智能门锁房屋比例 89% 租客首次租房时间最大值(月) 99%
5 配备智能门锁房屋比例 59% 评论数 87% 租客注册时间最大值(月) 99%
6 租客最近租房时间均值(月) 58% 租客注册时间最小值(年) 86% 配备智能门锁房屋比例 91%
7 金牛座租客个数 52% 接待境外人士房屋比例 79% 评论数 90%
8 租客注册时间均值(月) 51% 房屋先住后付比例 76% 租客注册时间均值(月) 89%
9 房屋标记“优品”比例 48% 射手座租客个数 75% 房东昵称是否含字母 88%
10 房东性别 48% 房东昵称是否含字母 73% 含宽度大于1.8m规格床房屋比例 77%
Top 10 Most Frequent Features in Feature Subsets
[1] Global Times. Squatters Occupy Venice Homes in Housing Protest as Tourism Surges[EB/OL]. [2020-01-01]. http://www.globaltimes.cn/content/1146303.shtml.
[2] Global Times. Xiaozhu Raises $300 Million[EB/OL]. [2020-01-01]. http://www.globaltimes.cn/content/1122433.shtml.
[3] 国家信息中心. 国家信息中心发布《中国共享住宿发展报告2019》[EB/OL]. http://www.sic.gov.cn/news/79/10105.htm.
[3] ( Chinese National Information Center. Chinese National Information Center Announced “the 2019 Chinese Sharing Accomodation Development Report” [EB/OL]. http://www.sic.gov.cn/news/79/10105.htm.
[4] Sutherland W, Jarrahi M H. The Sharing Economy and Digital Platforms: A Review and Research Agenda[J]. International Journal of Information Management, 2018,43:328-341.
doi: 10.1016/j.ijinfomgt.2018.07.004
[5] Yang S B, Lee K, Lee H, et al. In Airbnb We Trust: Understanding Consumers’ Trust-Attachment Building Mechanisms in the Sharing Economy[J]. International Journal of Hospitality Management, 2019,83:198-209.
doi: 10.1016/j.ijhm.2018.10.016
[6] Huang D, Coghlan A, Jin X. Understanding the Drivers of Airbnb Discontinuance[J]. Annals of Tourism Research, 2020,80:102798.
doi: 10.1016/j.annals.2019.102798
[7] US TODAY. Deaths at Airbnb Rentals Put Spotlight on Safety and Security[EB/OL]. [2020-01-01].https://www.usatoday.com/story/travel/2018/12/11/airbnb-security-mexico-costa-rica-deaths/2239544002/.
[8] US TODAY. Airbnb Plans to Ban ‘Party Houses’ after Orinda Shooting. Now People Are Asking How[EB/OL]. [2020-01-01].https://www.usatoday.com/story/travel/hotels/2019/11/04/orinda-airbnb-shooting-party-house-ban-incites-questions-skeptics/4154696002/.
[9] CNN. Family Finds Hidden Camera Livestreaming from Their Airbnb in Ireland[EB/OL]. [2020-01-01].https://edition.cnn.com/2019/04/05/europe/ireland-airbnb-hidden-camera-scli-intl/index.html.
[10] CNN. British Couple Spends $11,800 on Airbnb Rental in Ibiza That doesn’t Exist[EB/OL]. [2020-01-01].https://edition.cnn.com/travel/article/airbnb-ibiza-spain-penthouse-scam-trnd/index.html.
[11] 央视网. 共享住宿惹“吐槽”:房源图与实际不符存卫生问题[EB/OL]. [2020-01-01]. http://news.cctv.com/2019/04/18/ARTIwEAIZ4BQVSntrpS2hSja190418.shtml.
[11] ( CCTV.com.Sharing Accommodation Gets “Criticisms”: Pictures of House are not in Accordance with the Actual Situation, There are Health Issues[EB/OL]. [2020-01-01]. http://news.cctv.com/2019/04/18/ARTIwEAIZ4BQVSntrpS2hSja190418.shtml.
[12] Mao Z X, Jones M F, Li M M, et al. Sleeping in a Stranger’s Home: A Trust Formation Model for Airbnb[J]. Journal of Hospitality and Tourism Management, 2020,42:67-76.
doi: 10.1016/j.jhtm.2019.11.012
[13] So K K F, Oh H, Min S. Motivations and Constraints of Airbnb Consumers: Findings from a Mixed-Methods Approach[J]. Tourism Management, 2018,67:224-236.
doi: 10.1016/j.tourman.2018.01.009
[14] Califf C B, Brooks S, Longstreet P. Human-like and System-like Trust in the Sharing Economy: The Role of Context and Humanness[J]. Technological Forecasting and Social Change, 2020,154:119968.
doi: 10.1016/j.techfore.2020.119968
[15] Ert E, Fleischer A. The Evolution of Trust in Airbnb: A Case of Home Rental[J]. Annals of Tourism Research, 2019,75:279-287.
doi: 10.1016/j.annals.2019.01.004
[16] Moreno-Izquierdo L, Ramón-Rodríguez A B, Such-Devesa M J, et al. Tourist Environment and Online Reputation as a Generator of Added Value in the Sharing Economy: The Case of Airbnb in Urban and Sun- and-Beach Holiday Destinations[J]. Journal of Destination Marketing & Management, 2019,11:53-66.
[17] 赵建欣, 朱阁, 宋玲玉. 在线短租平台用户住宿决策影响因素研究[J]. 北京邮电大学学报(社会科学版), 2017,19(5):52-57.
[17] ( Zhao Jianxin, Zhu Ge, Song Lingyu. Influencing Factors of User Decision via Online Short-rent Platform[J]. Journal of Beijing University of Posts and Telecommunications (Social Sciences Edition), 2017,19(5):52-57.)
[18] Chattopadhyay M, Mitra S K. Do Airbnb Host Listing Attributes Influence Room Pricing Homogenously?[J]. International Journal of Hospitality Management, 2019,81:54-64.
doi: 10.1016/j.ijhm.2019.03.008
[19] 梁晓蓓, 徐真, 李晶晶. 共享短租平台商家属性对消费者网络口碑的影响研究[J]. 数据分析与知识发现, 2018,2(11):46-53.
[19] ( Liang Xiaobei, Xu Zhen, Li Jingjing. Impacts of Landlords on Tenants of Short-term Rentals[J]. Data Analysis and Knowledge Discovery, 2018,2(11):46-53.)
[20] Zhu Y X, Cheng M M, Wang J, et al. The Construction of Home Feeling by Airbnb Guests in the Sharing Economy: A Semantics Perspective[J]. Annals of Tourism Research, 2019,75:308-321.
doi: 10.1016/j.annals.2018.12.013
[21] Sthapit E, Björk P. Sources of Distrust: Airbnb Guests’ Perspectives[J]. Tourism Management Perspectives, 2019,31:245-253.
doi: 10.1016/j.tmp.2019.05.009
[22] Liang S, Li H, Liu X W, et al. Motivators Behind Information Disclosure: Evidence from Airbnb Hosts[J]. Annals of Tourism Research, 2019,76:305-319.
doi: 10.1016/j.annals.2019.03.001
[23] Tussyadiah I P, Zach F. Identifying Salient Attributes of Peer-to-Peer Accommodation Experience[J]. Journal of Travel & Tourism Marketing, 2017,34(5):636-652.
[24] Cheng M M, Jin X. What do Airbnb Users Care About? An Analysis of Online Review Comments[J]. International Journal of Hospitality Management, 2019,76:58-70.
doi: 10.1016/j.ijhm.2018.04.004
[25] Tussyadiah I P, Park S. When Guests Trust Hosts for Their Words: Host Description and Trust in Sharing Economy[J]. Tourism Management, 2018,67:261-272.
doi: 10.1016/j.tourman.2018.02.002
[26] Ert E, Fleischer A, Magen N. Trust and Reputation in the Sharing Economy: The Role of Personal Photos in Airbnb[J]. Tourism Management, 2016,55:62-73.
doi: 10.1016/j.tourman.2016.01.013
[27] Fagerstrøm A, Pawar S, Sigurdsson V, et al. That Personal Profile Image Might Jeopardize Your Rental Opportunity! On the Relative Impact of the Seller’s Facial Expressions upon Buying Behavior on Airbnb™[J]. Computers in Human Behavior, 2017,72:123-131.
doi: 10.1016/j.chb.2017.02.029
[28] 吴江, 靳萌萌. 在线短租房源图片对消费者行为意愿的影响[J]. 数据分析与知识发现, 2017,1(12):10-20.
[28] ( Wu Jiang, Jin Mengmeng. Online Room Listing Photos Affect Consumer’s Intentions[J]. Data Analysis and Knowledge Discovery, 2017,1(12):10-20.)
[29] Zhang L, Yan Q, Zhang L H. A Computational Framework for Understanding Antecedents of Guests’ Perceived Trust Towards Hosts on Airbnb[J]. Decision Support Systems, 2018,115:105-116.
doi: 10.1016/j.dss.2018.10.002
[30] Lu W, Stepchenkova S. User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software[J]. Journal of Hospitality Marketing & Management, 2015,24(2):119-154.
[31] Chen C C, Chang Y C. What Drives Purchase Intention on Airbnb? Perspectives of Consumer Reviews, Information Quality, and Media Richness[J]. Telematics and Informatics, 2018,35(5):1512-1523.
doi: 10.1016/j.tele.2018.03.019
[32] Liu S Q, Mattila A S. Airbnb: Online Targeted Advertising, Sense of Power, and Consumer Decisions[J]. International Journal of Hospitality Management, 2017,60:33-41.
doi: 10.1016/j.ijhm.2016.09.012
[33] McKnight D H, Chervany N L. What Trust Means in E-Commerce Customer Relationships: An Interdisciplinary Conceptual Typology[J]. International Journal of Electronic Commerce, 2001,6(2):35-59.
doi: 10.1080/10864415.2001.11044235
[34] McKnight D H, Choudhury V, Kacmar C. The Impact of Initial Consumer Trust on Intentions to Transact with a Web Site: A Trust Building Model[J]. The Journal of Strategic Information Systems, 2002,11(3):297-323.
doi: 10.1016/S0963-8687(02)00020-3
[35] McKnight D H, Tripp J. Technology, Humanness, and Trust: Rethinking Trust in Technology[J]. Journal of the Association for Information Systems, 2015,16:880-918.
doi: 10.17705/1jais
[36] Villeneuve H, O’Brien W. Listen to the Guests: Text-Mining Airbnb Reviews to Explore Indoor Environmental Quality[J]. Building and Environment, 2020,169:106555.
doi: 10.1016/j.buildenv.2019.106555
[37] Parrott W G. Emotions in Social Psychology: Essential Readings[M]. Psychology Press, 2001.
[38] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810.04805.
[39] d'Aspremont A, Ghaoui L E, Jordan M I, et al. A Direct Formulation of Sparse PCA Using Semidefinite Programming[J]. SIAM Review, 2007,49(3):434-448.
doi: 10.1137/050645506
[40] Li J D, Cheng K W, Wang S H, et al. Feature Selection: A Data Perspective[J]. ACM Computing Surveys, 2016, 50(6): Article No. 94.
[41] Zhao J, Liang J M, Dong Z N, et al. NEC: A Nested Equivalence Class-Based Dependency Calculation Approach for Fast Feature Selection Using Rough Set Theory[J]. Information Sciences, 2020,536:431-453.
doi: 10.1016/j.ins.2020.03.092
[42] Raza M S, Qamar U. An Incremental Dependency Calculation Technique for Feature Selection Using Rough Sets[J]. Information Sciences, 2016, 343-344:41-65.
doi: 10.1016/j.ins.2016.01.044
[43] Mafarja M, Aljarah I, Faris H, et al. Binary Grasshopper Optimisation Algorithm Approaches for Feature Selection Problems[J]. Expert Systems with Applications, 2018,117:267-286.
doi: 10.1016/j.eswa.2018.09.015
[44] Mafarja M, Mirjalili S. Whale Optimization Approaches for Wrapper Feature Selection[J]. Applied Soft Computing, 2018,62:441-453.
doi: 10.1016/j.asoc.2017.11.006
[45] Zhang Y, Wang Q, Gong D W, et al. Nonnegative Laplacian Embedding Guided Subspace Learning for Unsupervised Feature Selection[J]. Pattern Recognition, 2019,93:337-352.
doi: 10.1016/j.patcog.2019.04.020
[46] Liu Y F, Ye D Y, Li W B, et al. Robust Neighborhood Embedding for Unsupervised Feature Selection[J]. Knowledge-Based Systems, 2020,193:105462.
doi: 10.1016/j.knosys.2019.105462
[47] Zawbaa H M, Emary E, Grosan C, et al. Large-Dimensionality Small-Instance Set Feature Selection: A Hybrid Bio-Inspired Heuristic Approach[J]. Swarm and Evolutionary Computation, 2018,42:29-42.
doi: 10.1016/j.swevo.2018.02.021
[48] Wang Y W, Feng L Z. Hybrid Feature Selection Using Component Co-Occurrence Based Feature Relevance Measurement[J]. Expert Systems with Applications, 2018,102:83-99.
doi: 10.1016/j.eswa.2018.01.041
[49] Pawlak Z, Skowron A. Rudiments of Rough Sets[J]. Information Sciences, 2007,177(1):3-27.
doi: 10.1016/j.ins.2006.06.003
[50] Fayyad U M, Irani K B. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning[J]. Machine Learning, 1993,2:1022-1027.
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[8] Li Xiangdong,Ruan Tao,Liu Kang. Automatic Classification of Documents from Wikipedia[J]. 数据分析与知识发现, 2017, 1(10): 43-52.
[9] Lu Yonghe,Chen Jinghuang. Optimizing Feature Selection Method for Text Classification with Shuffled Frog Leaping Algorithm[J]. 数据分析与知识发现, 2017, 1(1): 91-101.
[10] Liu Hongguang,Ma Shuanggang,Liu Guifeng. Classifying Chinese News Texts with Denoising Auto Encoder[J]. 现代图书情报技术, 2016, 32(6): 12-19.
[11] Meng Yuan,Wang Hongwei. Evaluating Online Reviews Based on Text Content Features[J]. 现代图书情报技术, 2016, 32(4): 40-47.
[12] Li Xiangdong, Ba Zhichao, Huang Li. Allocation and Multi-granularity[J]. 现代图书情报技术, 2015, 31(5): 42-49.
[13] Xu Dongdong, Wu Shaobo. An Improved TF-IDF Feature Selection Based on Categorical Description[J]. 现代图书情报技术, 2015, 31(3): 39-48.
[14] Tan Xueqing, Zhou Tong, Luo Lin. A Text Classification Algorithm Based on the Average Category Similarity[J]. 现代图书情报技术, 2014, 30(9): 66-73.
[15] Gu Xiaoxue, Zhang Chengzhi. Using Content and Tags for Web Text Clustering[J]. 现代图书情报技术, 2014, 30(11): 45-52.
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