{"title":"通过时间序列分析检测季节性查询","authors":"Milad Shokouhi","doi":"10.1145/2009916.2010104","DOIUrl":null,"url":null,"abstract":"Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often reflected in search logs in form of seasonal spikes in the frequency of related queries (e.g. \"halloween costumes\", \"where is santa\"). Many seasonal queries such as \"sigir conference\" mainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g.sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"81","resultStr":"{\"title\":\"Detecting seasonal queries by time-series analysis\",\"authors\":\"Milad Shokouhi\",\"doi\":\"10.1145/2009916.2010104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often reflected in search logs in form of seasonal spikes in the frequency of related queries (e.g. \\\"halloween costumes\\\", \\\"where is santa\\\"). Many seasonal queries such as \\\"sigir conference\\\" mainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g.sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.\",\"PeriodicalId\":356580,\"journal\":{\"name\":\"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"81\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2009916.2010104\",\"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 34th international ACM SIGIR conference on Research and development in Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2009916.2010104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting seasonal queries by time-series analysis
Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often reflected in search logs in form of seasonal spikes in the frequency of related queries (e.g. "halloween costumes", "where is santa"). Many seasonal queries such as "sigir conference" mainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g.sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.