网络搜索相关性的在线指标

Jan O. Pedersen
{"title":"网络搜索相关性的在线指标","authors":"Jan O. Pedersen","doi":"10.1145/2513150.2513165","DOIUrl":null,"url":null,"abstract":"Information Retrieval has a long tradition of being metrics driven. Ranking algorithms are assessed with respect to some utility measure that reflects the likelihood of satisfying an information need. Traditionally these metrics are based on offline judgments. This is very flexible since judgments can be made for any desired output. However, judgments are no better than judgment guidelines and are at some distance from the actual user experience. Modern Web Search engines enjoy an additional resource; existing web search traffic and its attendant wealth of user engagement data. Primarily this signal consists of logged queries and user actions, including clicks and reformulations. I will discuss how this data can be used to derive Web Search quality metrics that have very different properties than traditional offline metrics.","PeriodicalId":436800,"journal":{"name":"LivingLab '13","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Online metrics for web search relevance\",\"authors\":\"Jan O. Pedersen\",\"doi\":\"10.1145/2513150.2513165\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Information Retrieval has a long tradition of being metrics driven. Ranking algorithms are assessed with respect to some utility measure that reflects the likelihood of satisfying an information need. Traditionally these metrics are based on offline judgments. This is very flexible since judgments can be made for any desired output. However, judgments are no better than judgment guidelines and are at some distance from the actual user experience. Modern Web Search engines enjoy an additional resource; existing web search traffic and its attendant wealth of user engagement data. Primarily this signal consists of logged queries and user actions, including clicks and reformulations. I will discuss how this data can be used to derive Web Search quality metrics that have very different properties than traditional offline metrics.\",\"PeriodicalId\":436800,\"journal\":{\"name\":\"LivingLab '13\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"LivingLab '13\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2513150.2513165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"LivingLab '13","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2513150.2513165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

信息检索具有度量驱动的悠久传统。排名算法是根据反映满足信息需求的可能性的一些效用度量来评估的。传统上,这些指标是基于离线判断。这是非常灵活的,因为可以对任何期望的输出做出判断。然而,判断并不比判断指南更好,并且与实际用户体验有一定距离。现代网络搜索引擎享有额外的资源;现有的网络搜索流量和随之而来的丰富的用户参与数据。这个信号主要由记录的查询和用户操作组成,包括点击和重新格式化。我将讨论如何使用这些数据来派生与传统离线度量具有非常不同属性的Web搜索质量度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online metrics for web search relevance
Information Retrieval has a long tradition of being metrics driven. Ranking algorithms are assessed with respect to some utility measure that reflects the likelihood of satisfying an information need. Traditionally these metrics are based on offline judgments. This is very flexible since judgments can be made for any desired output. However, judgments are no better than judgment guidelines and are at some distance from the actual user experience. Modern Web Search engines enjoy an additional resource; existing web search traffic and its attendant wealth of user engagement data. Primarily this signal consists of logged queries and user actions, including clicks and reformulations. I will discuss how this data can be used to derive Web Search quality metrics that have very different properties than traditional offline metrics.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信