{"title":"在野外顶级零售网页的感知性能","authors":"Qingzhu Gao, Prasenjit Dey, P. Ahammad","doi":"10.1145/3155055.3155062","DOIUrl":null,"url":null,"abstract":"Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the \\emph{Speed} of a page. In this paper we present \\emph{SpeedPerception}, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process.\n In Phase-1 of our \\emph{SpeedPerception} study using Internet Retailer Top 500 (IR 500) websites, we found that commonly used navigation metrics such as \\emph{onLoad} and \\emph{Time To First Byte (TTFB)} fail (less than 60\\% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with $87 \\pm 2\\%$ accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its \\emph{visualComplete} event.","PeriodicalId":403234,"journal":{"name":"Comput. Commun. Rev.","volume":"132 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Perceived Performance of Top Retail Webpages In the Wild\",\"authors\":\"Qingzhu Gao, Prasenjit Dey, P. Ahammad\",\"doi\":\"10.1145/3155055.3155062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the \\\\emph{Speed} of a page. In this paper we present \\\\emph{SpeedPerception}, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process.\\n In Phase-1 of our \\\\emph{SpeedPerception} study using Internet Retailer Top 500 (IR 500) websites, we found that commonly used navigation metrics such as \\\\emph{onLoad} and \\\\emph{Time To First Byte (TTFB)} fail (less than 60\\\\% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with $87 \\\\pm 2\\\\%$ accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its \\\\emph{visualComplete} event.\",\"PeriodicalId\":403234,\"journal\":{\"name\":\"Comput. Commun. Rev.\",\"volume\":\"132 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Comput. Commun. Rev.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3155055.3155062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Comput. Commun. Rev.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3155055.3155062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
摘要
显然,没有人喜欢低质量体验(QoE)的网页。被感知为慢或快是web应用程序整体感知QoE的关键因素。虽然在优化web应用程序方面已经投入了大量的努力(无论是在工业界还是学术界),但在描述网页加载过程的哪些方面真正影响人类最终用户对页面\emph{速度}的感知方面并没有很多工作。在本文中,我们提出了\emph{SpeedPerception},这是一个大规模的web性能众包框架,专注于理解折叠上(ATF)网页内容的感知加载性能。我们的最终目标是创建免费的开源基准数据集,以推进人类如何感知网页加载过程的系统分析。在我们使用互联网零售商500强(IR 500)网站进行的\emph{速度感知}研究的第一阶段,我们发现,在比较两个网页的速度时,常用的导航指标,如\emph{onLoad}和\emph{Time To First Byte (TTFB)}无法代表大多数人的感知(匹配率低于60%)。我们提出了一个简单的基于3变量的机器学习模型,可以更好地解释大多数最终用户的选择(准确度为$87 \pm 2\%$)。此外,我们的结果表明,最终用户评估网页的相对感知速度所需的时间远远少于其\emph{visualComplete}事件的时间。
Perceived Performance of Top Retail Webpages In the Wild
Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the \emph{Speed} of a page. In this paper we present \emph{SpeedPerception}, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process.
In Phase-1 of our \emph{SpeedPerception} study using Internet Retailer Top 500 (IR 500) websites, we found that commonly used navigation metrics such as \emph{onLoad} and \emph{Time To First Byte (TTFB)} fail (less than 60\% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with $87 \pm 2\%$ accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its \emph{visualComplete} event.