A Neural Click Model for Web Search

Alexey Borisov, I. Markov, M. de Rijke, P. Serdyukov
{"title":"A Neural Click Model for Web Search","authors":"Alexey Borisov, I. Markov, M. de Rijke, P. Serdyukov","doi":"10.1145/2872427.2883033","DOIUrl":null,"url":null,"abstract":"Understanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented as a sequence of observable and hidden events. The PGM framework provides a mathematically solid way to reason about a set of events given some information about other events. But the structure of the dependencies between the events has to be set manually. Different click models use different hand-crafted sets of dependencies. We propose an alternative based on the idea of distributed representations: to represent the user's information need and the information available to the user with a vector state. The components of the vector state are learned to represent concepts that are useful for modeling user behavior. And user behavior is modeled as a sequence of vector states associated with a query session: the vector state is initialized with a query, and then iteratively updated based on information about interactions with the search engine results. This approach allows us to directly understand user browsing behavior from click-through data, i.e., without the need for a predefined set of rules as is customary for PGM-based click models. We illustrate our approach using a set of neural click models. Our experimental results show that the neural click model that uses the same training data as traditional PGM-based click models, has better performance on the click prediction task (i.e., predicting user click on search engine results) and the relevance prediction task (i.e., ranking documents by their relevance to a query). An analysis of the best performing neural click model shows that it learns similar concepts to those used in traditional click models, and that it also learns other concepts that cannot be designed manually.","PeriodicalId":20455,"journal":{"name":"Proceedings of the 25th International Conference on World Wide Web","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"166","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th International Conference on World Wide Web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2872427.2883033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 166

Abstract

Understanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented as a sequence of observable and hidden events. The PGM framework provides a mathematically solid way to reason about a set of events given some information about other events. But the structure of the dependencies between the events has to be set manually. Different click models use different hand-crafted sets of dependencies. We propose an alternative based on the idea of distributed representations: to represent the user's information need and the information available to the user with a vector state. The components of the vector state are learned to represent concepts that are useful for modeling user behavior. And user behavior is modeled as a sequence of vector states associated with a query session: the vector state is initialized with a query, and then iteratively updated based on information about interactions with the search engine results. This approach allows us to directly understand user browsing behavior from click-through data, i.e., without the need for a predefined set of rules as is customary for PGM-based click models. We illustrate our approach using a set of neural click models. Our experimental results show that the neural click model that uses the same training data as traditional PGM-based click models, has better performance on the click prediction task (i.e., predicting user click on search engine results) and the relevance prediction task (i.e., ranking documents by their relevance to a query). An analysis of the best performing neural click model shows that it learns similar concepts to those used in traditional click models, and that it also learns other concepts that cannot be designed manually.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
网络搜索的神经点击模型
了解用户在网络搜索中的浏览行为是提高网络搜索效率的关键。已经提出了许多点击模型来解释或预测用户对搜索引擎结果的点击。它们基于概率图形模型(PGM)框架,在该框架中,用户行为被表示为一系列可观察和隐藏的事件。PGM框架提供了一种数学上可靠的方法,在给定关于其他事件的一些信息的情况下对一组事件进行推理。但是事件之间的依赖关系的结构必须手工设置。不同的点击模型使用不同的手工制作的依赖集。我们提出了一种基于分布式表示思想的替代方案:用向量状态表示用户的信息需求和用户可用的信息。学习向量状态的组件来表示对建模用户行为有用的概念。用户行为被建模为与查询会话相关联的向量状态序列:向量状态用查询初始化,然后根据与搜索引擎结果交互的信息迭代更新。这种方法允许我们从点击数据中直接理解用户的浏览行为,也就是说,不需要像基于pgm的点击模型那样使用一组预定义的规则。我们使用一组神经点击模型来说明我们的方法。我们的实验结果表明,神经点击模型与传统的基于pgm的点击模型使用相同的训练数据,在点击预测任务(即预测用户对搜索引擎结果的点击)和相关性预测任务(即根据与查询的相关性对文档进行排序)上具有更好的性能。对表现最好的神经点击模型的分析表明,它学习了与传统点击模型相似的概念,并且它还学习了其他无法手动设计的概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
MapWatch: Detecting and Monitoring International Border Personalization on Online Maps Automatic Discovery of Attribute Synonyms Using Query Logs and Table Corpora Learning Global Term Weights for Content-based Recommender Systems From Freebase to Wikidata: The Great Migration GoCAD: GPU-Assisted Online Content-Adaptive Display Power Saving for Mobile Devices in Internet Streaming
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1