查询式序列视角下跨设备搜索信息准备与重用阶段的眼动变化研究
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摘要
在信息检索领域中,用户在搜索引擎结果页面的眼动行为一直受到研究者的关注;特别是在跨设备搜索、跨屏交互日益普遍的背景下,用户发生设备转移后在SERP上的眼动行为分析已成为一个研究热点。在复杂搜索任务中,用户会提交多个查询式,而现有研究缺乏基于查询式序列的角度分析用户SERP眼动行为。本文通过在实验室环境下,开展用户实验,收集用户在跨设备搜索任务中的信息准备和信息重用两个阶段的眼动数据,从基本眼动现象、时间维度现象、空间维度现象三个层次,分析实验用户在SERP上眼动注视行为的变化。研究发现"信息重用"阶段中,用户在SERP上的注视时长、关注点数量,相比"信息准备"阶段都有所降低;注视区域的演化规律也发生了变化;眼跳次数、回视次数等指标也普遍降低;支持用户跨设备网络搜索的工具能够有效降低用户的眼动视觉负荷。
The visual gaze behavior of SERP(search engine result pages) users is important in the field of information retrieval. Specifically, as the popularity of cross-screen interaction and cross-device web searching increases, the adjustments to a user's gaze behavior during device transition have become a hot research topic. Users will submit a series of queries when facing a complex search task, but research is lacking about the users gaze behavior based on their perspectives of those queries. This paper collects eye movement data while performing cross-device web searches during user experiments, including basic eye movement, time dimension, and spatial dimension phenomena. The results reveal that both the gaze duration and gaze count in"information resumption"are lower than in"information preparation."The evolution of the gaze region also changed, and both the saccade and regression counts are generally reduced. In addition, tools that support a user's cross-device web search can effectively reduce eye movement load.
The visual gaze behavior of SERP(search engine result pages) users is important in the field of information retrieval. Specifically, as the popularity of cross-screen interaction and cross-device web searching increases, the adjustments to a user's gaze behavior during device transition have become a hot research topic. Users will submit a series of queries when facing a complex search task, but research is lacking about the users gaze behavior based on their perspectives of those queries. This paper collects eye movement data while performing cross-device web searches during user experiments, including basic eye movement, time dimension, and spatial dimension phenomena. The results reveal that both the gaze duration and gaze count in"information resumption"are lower than in"information preparation."The evolution of the gaze region also changed, and both the saccade and regression counts are generally reduced. In addition, tools that support a user's cross-device web search can effectively reduce eye movement load.
引文
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[3] Wu D, Dong J, Tang Y. Identifying and modeling information resumption behaviors in cross-device search[C]//Proceedings of41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press,2018:1189-1192.
[4]胡蓉,赵宇翔,朱庆华.移动互联环境下用户跨屏行为整合分析框架——基于扎根理论的探索[J].中国图书馆学报, 2017, 43(6):113-129.
[5]吴丹,刘春香.基于情境的跨设备搜索需求研究[J].情报资料工作, 2018(1):45-52.
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[11] Wu D, Liang S B, Bi R M. Characterizing queries in cross-device OPAC search:A large-scale log study[J]. Library Hi Tech, 2018,36(3):482-497.
[12] Kay B M, Watters C. Exploring multi-session Web tasks[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2008:1187-1196.
[13] Han S G, Yue Z, He D Q. Understanding and supporting cross-device Web search for exploratory tasks with mobile touch interactions[J]. ACM Transactions on Information Systems, 2015, 33(4):Article No. 16.
[14] Wu D, Dong J, Tang Y. Modeling and analyzing information preparation behaviors in cross-device search[C]//Proceedings of the International Conference on Cross-Cultural Design. Cham:Springer, 2018, 10911:232-249.
[15] Joho H, Jose J M. A comparative study of the effectiveness of search result presentation on the Web[C]//Proceedings of the European Conference on Information Retrieval. Heidelberg:Springer, 2006, 3936:302-313.
[16] Kim J, Thomas P, Sankaranarayana R, et al. Eye-tracking analysis of user behavior and performance in Web search on large and small screens[J]. Journal of the Association for Information Science and Technology, 2015, 66(3):526-544.
[17] Kelly D, Azzopardi L. How many results per page?:A study of SERP size, search behavior and user experience[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2015:183-192.
[18] Diaz F, White R, Buscher G, et al. Robust models of mouse movement on dynamic web search results pages[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2013:1451-1460.
[19]梁少博,吴丹,董晶,等.跨设备搜索引擎结果页面注意力分布研究——基于眼动视觉数据的实证分析[J].图书情报知识,2018(1):27-35, 15.
[20]许鑫,曹阳.基于眼动追踪实验的高校图书馆门户网站网页设计研究[J].大学图书馆学报, 2017(3):46-52.
[21] Wang Y, Huang X, White R W. Characterizing and supporting cross-device search tasks[C]//Proceedings of the ACM International Conference on Web Search and Data Mining. New York:ACM Press, 2013:707-716.
[22]胡蓉.移动搜索情境下的跨屏行为概念框架[J].情报理论与实践, 2018, 41(1):66-70, 86.
[23]吴丹,陆柳杏.跨设备搜索中设备转移前后查询式语义变化研究[J].数据分析与知识发现, 2018, 2(8):69-78.
[24]毕仁敏.多设备环境下图书馆OPAC查询重构研究[D].武汉:武汉大学, 2018.
[25] Agichtein E, White R W, Dumais S T, et al. Search, interrupted:Understanding and predicting search task continuation[C]//Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2012:315-324.
[26]闫国利,熊建萍,臧传丽,等.阅读研究中的主要眼动指标评述[J].心理科学进展, 2013, 21(4):589-605.
[27]宋洋洋,吕胜富,栗觅,等.网页信息位置对视觉信息搜索影响的眼动研究[C]//第九届中国Rough集与软计算、第三届中国Web智能、第三届中国粒计算联合会议论文集.北京:中国计算机学会,中国人工智能学会, 2009:198-201.
[28] Bataineh E, Al-Bataineh B. An analysis study on how female college students view the web search results using eye tracking methodology[C]//Proceedings of the 2nd International Conference on Human-Computer Interaction. 2014:Paper No. 93.
[29] Guan Z W, Cutrell E. An eye tracking study of the effect of target rank on Web search[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2007:417-420.
[30]卢万譞,梁玮,贾云得.用户推荐对网络搜索行为影响的眼动研究[J].北京理工大学学报, 2016, 36(5):514-519.
[31] Nettleton D F, González-Caro C. Analysis of user behavior for Web search success using eye tracker data[C]//Proceedings of the 2012 Eighth Latin American Web Congress. New York:ACM Press, 2012:57-63.
[32] Lagun D, Mcmahon D, Navalpakkam V. Understanding mobile searcher attention with rich ad formats[C]//Proceedings of the25th ACM International on Conference on Information and Knowledge Management. New York:ACM Press, 2016:599-608.
[33] Djamasbi S, Hall-Phillips A, Yang R. SERPs and ads on mobile devices:An eye tracking study for generation Y[C]//Proceedings of the International Conference on Universal Access in HumanComputer Interaction:User and Context Diversity. Heidelberg:Springer, 2013, 8010:259-268.
[34]朱郭奇,孙林岩,崔凯.基于眼动行为分析方法的平面广告效果影响因素研究[J].管理评论, 2012, 24(9):121-131.
[35]刘俊.基于眼动追踪的隐式相关反馈方法研究[D].北京:北京交通大学, 2014.
[36]程时伟,沈晓权,陆煜华,等.眼动跟踪实现的跨设备分布式注意力感知界面[J].计算机辅助设计与图形学学报, 2017, 29(9):1713-1724.
[37] Sellen A J, Murphy R, Shaw K L. How knowledge workers use the Web[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2002:227-234.
[38] Czerwinski M, Horvitz E, Wilhite S. A diary study of task switching and interruptions[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2004:175-182.
[39] Wu D, Yao X, Dong J, et al. Designing mobile search tasks:A context-based approach[J]. Geomatics and Information Science of Wuhan University, 2016, 41:34-39.
[40] Papoutsaki A, Laskey J, Huang J. SearchGazer:Webcam eye tracking for remote studies of Web search[C]//Proceedings of the2017 Conference on Conference Human Information Interaction and Retrieval. New York:ACM Press, 2017:17-26.
[41] Wu Y C, Liu S X, Yan K, et al. OpinionFlow:Visual analysis of opinion diffusion on social media[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1763-1772.
(1)http://crosssearch.whu.edu.cn
[2] Montanez G D, White R W, Huang X. Cross-device search[C]//Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. New York:ACM Press,2014:1669-1678.
[3] Wu D, Dong J, Tang Y. Identifying and modeling information resumption behaviors in cross-device search[C]//Proceedings of41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press,2018:1189-1192.
[4]胡蓉,赵宇翔,朱庆华.移动互联环境下用户跨屏行为整合分析框架——基于扎根理论的探索[J].中国图书馆学报, 2017, 43(6):113-129.
[5]吴丹,刘春香.基于情境的跨设备搜索需求研究[J].情报资料工作, 2018(1):45-52.
[6] Teevan J, Adar E, Jones R, et al. Information re-retrieval:Repeat queries in Yahoo’s logs[C]//Proceedings of 30th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2007:151-158.
[7] Tyler S K, Teevan J. Large scale query log analysis of re-finding[C]//Proceeding of the 3rd International ACM Conference on Web Search and Data Mining. New York:ACM Press, 2010:191-200.
[8] Yi J, Maghoul F, Pedersen J. Deciphering mobile search patterns:A study of Yahoo! mobile search queries[C]//Proceedings of the17th International Conference on World Wide Web. New York:ACM Press, 2008:257-266.
[9] Kotov A, Bennett P N, White R W, et al. Modeling and analysis of cross-session search tasks[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2011:5-14.
[10] Wikipedia. Web search query[EB/OL].[2018-09-12]. https://en.wikipedia.org/wiki/Web_search_query.
[11] Wu D, Liang S B, Bi R M. Characterizing queries in cross-device OPAC search:A large-scale log study[J]. Library Hi Tech, 2018,36(3):482-497.
[12] Kay B M, Watters C. Exploring multi-session Web tasks[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2008:1187-1196.
[13] Han S G, Yue Z, He D Q. Understanding and supporting cross-device Web search for exploratory tasks with mobile touch interactions[J]. ACM Transactions on Information Systems, 2015, 33(4):Article No. 16.
[14] Wu D, Dong J, Tang Y. Modeling and analyzing information preparation behaviors in cross-device search[C]//Proceedings of the International Conference on Cross-Cultural Design. Cham:Springer, 2018, 10911:232-249.
[15] Joho H, Jose J M. A comparative study of the effectiveness of search result presentation on the Web[C]//Proceedings of the European Conference on Information Retrieval. Heidelberg:Springer, 2006, 3936:302-313.
[16] Kim J, Thomas P, Sankaranarayana R, et al. Eye-tracking analysis of user behavior and performance in Web search on large and small screens[J]. Journal of the Association for Information Science and Technology, 2015, 66(3):526-544.
[17] Kelly D, Azzopardi L. How many results per page?:A study of SERP size, search behavior and user experience[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2015:183-192.
[18] Diaz F, White R, Buscher G, et al. Robust models of mouse movement on dynamic web search results pages[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2013:1451-1460.
[19]梁少博,吴丹,董晶,等.跨设备搜索引擎结果页面注意力分布研究——基于眼动视觉数据的实证分析[J].图书情报知识,2018(1):27-35, 15.
[20]许鑫,曹阳.基于眼动追踪实验的高校图书馆门户网站网页设计研究[J].大学图书馆学报, 2017(3):46-52.
[21] Wang Y, Huang X, White R W. Characterizing and supporting cross-device search tasks[C]//Proceedings of the ACM International Conference on Web Search and Data Mining. New York:ACM Press, 2013:707-716.
[22]胡蓉.移动搜索情境下的跨屏行为概念框架[J].情报理论与实践, 2018, 41(1):66-70, 86.
[23]吴丹,陆柳杏.跨设备搜索中设备转移前后查询式语义变化研究[J].数据分析与知识发现, 2018, 2(8):69-78.
[24]毕仁敏.多设备环境下图书馆OPAC查询重构研究[D].武汉:武汉大学, 2018.
[25] Agichtein E, White R W, Dumais S T, et al. Search, interrupted:Understanding and predicting search task continuation[C]//Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2012:315-324.
[26]闫国利,熊建萍,臧传丽,等.阅读研究中的主要眼动指标评述[J].心理科学进展, 2013, 21(4):589-605.
[27]宋洋洋,吕胜富,栗觅,等.网页信息位置对视觉信息搜索影响的眼动研究[C]//第九届中国Rough集与软计算、第三届中国Web智能、第三届中国粒计算联合会议论文集.北京:中国计算机学会,中国人工智能学会, 2009:198-201.
[28] Bataineh E, Al-Bataineh B. An analysis study on how female college students view the web search results using eye tracking methodology[C]//Proceedings of the 2nd International Conference on Human-Computer Interaction. 2014:Paper No. 93.
[29] Guan Z W, Cutrell E. An eye tracking study of the effect of target rank on Web search[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2007:417-420.
[30]卢万譞,梁玮,贾云得.用户推荐对网络搜索行为影响的眼动研究[J].北京理工大学学报, 2016, 36(5):514-519.
[31] Nettleton D F, González-Caro C. Analysis of user behavior for Web search success using eye tracker data[C]//Proceedings of the 2012 Eighth Latin American Web Congress. New York:ACM Press, 2012:57-63.
[32] Lagun D, Mcmahon D, Navalpakkam V. Understanding mobile searcher attention with rich ad formats[C]//Proceedings of the25th ACM International on Conference on Information and Knowledge Management. New York:ACM Press, 2016:599-608.
[33] Djamasbi S, Hall-Phillips A, Yang R. SERPs and ads on mobile devices:An eye tracking study for generation Y[C]//Proceedings of the International Conference on Universal Access in HumanComputer Interaction:User and Context Diversity. Heidelberg:Springer, 2013, 8010:259-268.
[34]朱郭奇,孙林岩,崔凯.基于眼动行为分析方法的平面广告效果影响因素研究[J].管理评论, 2012, 24(9):121-131.
[35]刘俊.基于眼动追踪的隐式相关反馈方法研究[D].北京:北京交通大学, 2014.
[36]程时伟,沈晓权,陆煜华,等.眼动跟踪实现的跨设备分布式注意力感知界面[J].计算机辅助设计与图形学学报, 2017, 29(9):1713-1724.
[37] Sellen A J, Murphy R, Shaw K L. How knowledge workers use the Web[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2002:227-234.
[38] Czerwinski M, Horvitz E, Wilhite S. A diary study of task switching and interruptions[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York:ACM Press, 2004:175-182.
[39] Wu D, Yao X, Dong J, et al. Designing mobile search tasks:A context-based approach[J]. Geomatics and Information Science of Wuhan University, 2016, 41:34-39.
[40] Papoutsaki A, Laskey J, Huang J. SearchGazer:Webcam eye tracking for remote studies of Web search[C]//Proceedings of the2017 Conference on Conference Human Information Interaction and Retrieval. New York:ACM Press, 2017:17-26.
[41] Wu Y C, Liu S X, Yan K, et al. OpinionFlow:Visual analysis of opinion diffusion on social media[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1763-1772.
(1)http://crosssearch.whu.edu.cn