Alexey Tikhonov, Ivan Bogatyy, Pavel Burangulov, L. Ostroumova, V. Koshelev, Gleb Gusev
{"title":"动态web中页面生命周期模式的研究","authors":"Alexey Tikhonov, Ivan Bogatyy, Pavel Burangulov, L. Ostroumova, V. Koshelev, Gleb Gusev","doi":"10.1145/2484028.2484185","DOIUrl":null,"url":null,"abstract":"With the ever-increasing speed of content turnover on the web, it is particularly important to understand the patterns that pages' popularity follows. This paper focuses on the dynamical part of the web, i.e. pages that have a limited lifespan and experience a short popularity outburst within it. We classify these pages into five patterns based on how quickly they gain popularity and how quickly they lose it. We study the properties of pages that belong to each pattern and determine content topics that contain disproportionately high fractions of particular patterns. These developments are utilized to create an algorithm that approximates with reasonable accuracy the expected popularity pattern of a web page based on its URL and, if available, prior knowledge about its domain's topics.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Studying page life patterns in dynamical web\",\"authors\":\"Alexey Tikhonov, Ivan Bogatyy, Pavel Burangulov, L. Ostroumova, V. Koshelev, Gleb Gusev\",\"doi\":\"10.1145/2484028.2484185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the ever-increasing speed of content turnover on the web, it is particularly important to understand the patterns that pages' popularity follows. This paper focuses on the dynamical part of the web, i.e. pages that have a limited lifespan and experience a short popularity outburst within it. We classify these pages into five patterns based on how quickly they gain popularity and how quickly they lose it. We study the properties of pages that belong to each pattern and determine content topics that contain disproportionately high fractions of particular patterns. These developments are utilized to create an algorithm that approximates with reasonable accuracy the expected popularity pattern of a web page based on its URL and, if available, prior knowledge about its domain's topics.\",\"PeriodicalId\":178818,\"journal\":{\"name\":\"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2484028.2484185\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2484028.2484185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the ever-increasing speed of content turnover on the web, it is particularly important to understand the patterns that pages' popularity follows. This paper focuses on the dynamical part of the web, i.e. pages that have a limited lifespan and experience a short popularity outburst within it. We classify these pages into five patterns based on how quickly they gain popularity and how quickly they lose it. We study the properties of pages that belong to each pattern and determine content topics that contain disproportionately high fractions of particular patterns. These developments are utilized to create an algorithm that approximates with reasonable accuracy the expected popularity pattern of a web page based on its URL and, if available, prior knowledge about its domain's topics.