{"title":"机器学习算法用于谷歌应用商店目录的比较","authors":"Priyadarshini Pattanaik, Dimple Nagpal","doi":"10.21037/jmai-23-58","DOIUrl":null,"url":null,"abstract":"Background: Google Play Store is a popular Android app store where users’ reviews and ratings provide valuable insights. As part of application development, clients and app designers have a significant impact on the market. Accurately predicting market trends is critical to the success of applications, and this is where information mining comes in. By evaluating various factors such as application name, pricing, reviews, and category, we can predict which types of apps are most likely to be successful.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of machine learning algorithms used to catalog Google Appstore\",\"authors\":\"Priyadarshini Pattanaik, Dimple Nagpal\",\"doi\":\"10.21037/jmai-23-58\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Google Play Store is a popular Android app store where users’ reviews and ratings provide valuable insights. As part of application development, clients and app designers have a significant impact on the market. Accurately predicting market trends is critical to the success of applications, and this is where information mining comes in. By evaluating various factors such as application name, pricing, reviews, and category, we can predict which types of apps are most likely to be successful.\",\"PeriodicalId\":73815,\"journal\":{\"name\":\"Journal of medical artificial intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medical artificial intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21037/jmai-23-58\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medical artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21037/jmai-23-58","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
背景:Google Play Store是一个受欢迎的Android应用商店，用户的评论和评级可以提供有价值的见解。作为应用程序开发的一部分，客户和应用程序设计人员对市场有重大影响。准确预测市场趋势对于应用程序的成功至关重要，而这正是信息挖掘的用武之地。通过评估应用名称、定价、评论和类别等各种因素，我们可以预测哪种类型的应用最有可能获得成功。
Comparison of machine learning algorithms used to catalog Google Appstore
Background: Google Play Store is a popular Android app store where users’ reviews and ratings provide valuable insights. As part of application development, clients and app designers have a significant impact on the market. Accurately predicting market trends is critical to the success of applications, and this is where information mining comes in. By evaluating various factors such as application name, pricing, reviews, and category, we can predict which types of apps are most likely to be successful.
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