{"title":"Picky Eaters Make for Better Raters","authors":"Sasha Stoikov, Stefano Borzillo, Steffen Raub","doi":"10.1177/19389655241226557","DOIUrl":null,"url":null,"abstract":"It has been established in the literature that the number of ratings and the scores restaurants obtain on online rating systems (ORS) significantly impact their revenue. However, when a restaurant has a limited number of ratings, it may be challenging to predict its future performance. It may well be that ratings reveal more about the user who gave the rating than about the quality of the restaurant. This motivates us to segment users into “inflating raters,” who tend to give unusually high ratings, and “deflating raters,” who tend to give unusually low ratings, and compare the rankings generated by these two populations. Using a public dataset provided by Yelp, we find that deflating raters are better at predicting restaurants that will achieve a top rating (4.5 and above) in the future. As such, these deflating raters may have an important role in restaurant discovery.","PeriodicalId":47888,"journal":{"name":"Cornell Hospitality Quarterly","volume":"129 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cornell Hospitality Quarterly","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/19389655241226557","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
引用次数: 0
Abstract
It has been established in the literature that the number of ratings and the scores restaurants obtain on online rating systems (ORS) significantly impact their revenue. However, when a restaurant has a limited number of ratings, it may be challenging to predict its future performance. It may well be that ratings reveal more about the user who gave the rating than about the quality of the restaurant. This motivates us to segment users into “inflating raters,” who tend to give unusually high ratings, and “deflating raters,” who tend to give unusually low ratings, and compare the rankings generated by these two populations. Using a public dataset provided by Yelp, we find that deflating raters are better at predicting restaurants that will achieve a top rating (4.5 and above) in the future. As such, these deflating raters may have an important role in restaurant discovery.
期刊介绍：
Cornell Hospitality Quarterly (CQ) publishes research in all business disciplines that contribute to management practice in the hospitality and tourism industries. Like the hospitality industry itself, the editorial content of CQ is broad, including topics in strategic management, consumer behavior, marketing, financial management, real-estate, accounting, operations management, planning and design, human resources management, applied economics, information technology, international development, communications, travel and tourism, and more general management. The audience is academics, hospitality managers, developers, consultants, investors, and students.
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