[1]王超,刘奕群,马少平.搜索引擎点击模型综述[J].智能系统学报,2016,11(6):711-718.[doi:10.11992/tis.201605023]
WANG Chao,LIU Yiqun,MA Shaoping.A survey of click models for Web browsing[J].CAAI Transactions on Intelligent Systems,2016,11(6):711-718.[doi:10.11992/tis.201605023]
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WANG Chao,LIU Yiqun,MA Shaoping.A survey of click models for Web browsing[J].CAAI Transactions on Intelligent Systems,2016,11(6):711-718.[doi:10.11992/tis.201605023]
搜索引擎点击模型综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第6期
页码:
711-718
栏目:
综述
出版日期:
2017-01-20
- Title:
- A survey of click models for Web browsing
- 分类号:
- TP391
- 摘要:
- 搜索引擎用户在与搜索引擎的交互过程中反映出的隐性反馈信息（主要是点击行为信息）是搜索引擎用来改进结果排序的重要影响因素。然而，由于结果位置、展现形式等各种因素的影响，将反馈信息直接应用于搜索排序任务往往难以取得较好的效果。针对这一问题，研究人员提出了构建描述用户点击行为的点击模型，并基于不同的点击模型估计用户对展现结果的浏览概率，进而尝试去除结果展现位置等因素对用户行为的偏置性影响，以达到更好利用隐性反馈信息的目的。作为一种用户交互信息的有效利用方法，点击模型在学术界得到了充分关注，并在工业界得到了广泛的应用。本文是一篇针对点击模型发展过程的综述性文章，对点击模型发展过程中有代表性的多种模型进行了介绍。
- Abstract:
- The implicit feedback information contained in a user’s search interaction process makes an important contribution to the improvement of search ranking. However, since user behavior is affected by several factors (or biases) caused by the ranked positions of the results, presentation styles, etc., it is difficult to directly adopt click information as a relevant feedback mechanism of the search sequence task. To shed light on this research question, researchers have proposed several click models to describe how users examine and click on results from the search engine result pages (SERPs). Based on these models, it is possible to estimate the examination probability of search results and thus reduce the influence of behavior biases to obtain a justified estimation of the result’s relevance. Much attention has been paid to the click model in recent years because it helps commercial search engines to improve ranking performance. In this paper, recent efforts made in constructing click models were investigated and their differences were compared in both performance and application scenarios.
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备注/Memo
收稿日期:2016-05-26。
基金项目:国家自然科学基金项目（61532011，61672311）.
作者简介:王超,男,1989年生,博士,主要研究方向为互联网搜索结果排序和用户行为建模方面的研究,发表学术论文多篇,获得SIGIR2015最佳论文提名奖;刘奕群,男,1981年生,副教授、博士生导师中国人工智能学会理事,知识工程与分布智能专委会委员,中国中文信息学会信息检索与内容安全专委会委员。主要研究方向为信息检索与互联网搜索技术。发表学术论文30余篇,获得SIGIR (CCF A类)最佳论文提名奖。据Google Scholar统计,论文被引用1700余次;马少平,男,1961年生,教授、博士生导师,中国人工智能学会副理事长,知识工程与分布式智能专委会主任,中国中文信息学会常务理事,信息检索与内容安全专委会副主任。主要研究方向为智能信息处理,模式识别、文本信息检索、中文古籍的数字化与检索。作为项目负责人先后承担"973"、"863"、自然科学基金项目等多项课题。所领导的文本信息检索小组,从2002年开始,在国际上著名的TREC (文本检索国际会议)文本检索标准评测中,多次取得第一名的好成绩,发表学术论文多篇。
通讯作者:马少平.E-mail:msp@tsinghua.edu.cn.
更新日期/Last Update:
1900-01-01