挑食者是更好的评分者

IF 3.4 4区管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM

Cornell Hospitality Quarterly Pub Date : 2024-02-07 DOI:10.1177/19389655241226557

Sasha Stoikov, Stefano Borzillo, Steffen Raub

{"title":"挑食者是更好的评分者","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":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":null,\"pages\":null},\"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}","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

摘要

已有文献证实，餐厅在在线评分系统（ORS）上获得的评分数量和分数会对其收入产生重大影响。然而，当一家餐厅获得的评分数量有限时，预测其未来的业绩可能会很困难。评分很可能更多地反映了给出评分的用户的情况，而不是餐厅的质量。这促使我们将用户分为 "夸大评分者 "和 "贬低评分者"，前者倾向于给出异常高的评分，后者倾向于给出异常低的评分，并比较这两种人群所产生的排名。通过使用 Yelp 提供的公开数据集，我们发现 "打折评分者 "更善于预测未来将获得最高评分（4.5 分及以上）的餐厅。因此，这些 "放水评分者 "可能在餐厅发现方面发挥着重要作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Picky Eaters Make for Better Raters

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Cornell Hospitality Quarterly Multiple-

CiteScore

8.50

自引率

2.90%

发文量

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.