{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:34:55Z","timestamp":1754156095462,"version":"3.41.2"},"reference-count":17,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2021,1,23]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Pricing on the online booking systems is a difficult task for the host, the systems usually set the prices that are lower than the general premises and quality, and that only gives benefits to the system by easily attracting the customer to use the service. The setting price of the new accommodation is often based on location, the number of beds, type of house and so on. The main problem is to predict the most reasonable price for the host. This paper aims to study the use of machine learning and sentiment analysis for predicting the price of online booking systems.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>In particular, an empirical study is performed first for some well-known classification models for the problems. The authors then propose to apply k-means, a clustering technique, together with Gradient Boost and XGBoost models to improve the prediction performance. Experiments are conducted and tested for real Airbnb data sets collected in London City.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Experimental results are given and compared to show that the authors\u2019 method outperforms to an updated method.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The authors use k-means and sampling together with Gradient Boost and XGBoost models to improve the prediction performance.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-11-2020-0065","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T01:52:56Z","timestamp":1610502776000},"page":"45-53","source":"Crossref","is-referenced-by-count":7,"title":["Clustering helps to improve price prediction in online booking systems"],"prefix":"10.1108","volume":"17","author":[{"given":"Le Hong","family":"Trang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tran Duong","family":"Huy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anh Ngoc","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"key2021043009174763300_ref001","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"Xgboost: a scalable tree boosting system","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","year":"2016"},{"key":"key2021043009174763300_ref002","unstructured":"Cox, M. (2019), \u201cInside airbnb\u201d, available at: http:\/\/insideairbnb.com\/london"},{"key":"key2021043009174763300_ref003","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"edition":"2nd ed.","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","year":"2001","key":"key2021043009174763300_ref004"},{"issue":"4","key":"key2021043009174763300_ref005","article-title":"A comparative study of the some methods used in constructing coresets for clustering large datasets","volume":"1","year":"2020","journal-title":"SN Computer Science"},{"issue":"8","key":"key2021043009174763300_ref006","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","year":"1997","journal-title":"Neural Computation"},{"year":"2019","key":"key2021043009174763300_ref007","article-title":"Airbnb price prediction using machine learning and sentiment analysis"},{"issue":"9","key":"key2021043009174763300_ref008","first-page":"2065","article-title":"A comprehensive analysis of deep regression","volume":"42","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"key2021043009174763300_ref009","doi-asserted-by":"crossref","first-page":"7038","DOI":"10.1109\/ChiCC.2016.7554467","article-title":"Reasonable price recommendation on airbnb using multi-scale clustering","volume-title":"2016 35th Chinese Control Conference (CCC)","year":"2016"},{"article-title":"House price prediction: hedonic price model vs artificial neural network","volume-title":"The 2004 NZARES Conference","year":"2004","key":"key2021043009174763300_ref010"},{"issue":"1","key":"key2021043009174763300_ref011","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TNN.2004.836229","article-title":"Data classification with radial basis function networks based on a novel kernel density estimation algorithm","volume":"16","year":"2005","journal-title":"IEEE Transactions on Neural Networks"},{"key":"key2021043009174763300_ref012","first-page":"1567","article-title":"An assessment of support vector machine kernel parameters using remotely sensed satellite data","volume-title":"IEEE Internationalon Recent Trends in Electronics, Information and Communication Technology (RTEICT)","year":"2016"},{"key":"key2021043009174763300_ref013","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0169-7161(04)24011-1","article-title":"Classification and regression trees, bagging, and boosting","volume":"24","year":"2005","journal-title":"Handbook of Statistics"},{"key":"key2021043009174763300_ref014","first-page":"1","article-title":"A farthest first traversal based sampling algorithm for k-clustering","volume-title":"2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM)","year":"2020"},{"key":"key2021043009174763300_ref015","first-page":"1936","article-title":"House price prediction using machine learning and neural networks","volume-title":"The Second International Conference on Inventive Communication and Computational Technologies (ICICCT)","year":"2018"},{"key":"key2021043009174763300_ref016","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ijhm.2016.12.007","article-title":"Price determinants of sharing economy based accommodation rental: a study of listings from 33 cities on airbnb.com","volume":"62","year":"2017","journal-title":"International Journal of Hospitality Management"},{"key":"key2021043009174763300_ref017","unstructured":"Yu, H. and Wu, J. (2016), \u201cReal estate price prediction with regression and classification\u201d, Technical report, Stanford."}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-11-2020-0065\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-11-2020-0065\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:24:25Z","timestamp":1753395865000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/17\/1\/45-53\/163916"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,8]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1,8]]},"published-print":{"date-parts":[[2021,1,23]]}},"alternative-id":["10.1108\/IJWIS-11-2020-0065"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-11-2020-0065","relation":{},"ISSN":["1744-0084","1744-0084"],"issn-type":[{"type":"print","value":"1744-0084"},{"type":"print","value":"1744-0084"}],"subject":[],"published":{"date-parts":[[2021,1,8]]}}}