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基于眼动数据的网络搜索行为预测方法

卢万譞 贾云得

卢万譞, 贾云得. 基于眼动数据的网络搜索行为预测方法[J]. 北京航空航天大学学报, 2015, 41(5): 904-910. doi: 10.13700/j.bh.1001-5965.2014.0464
引用本文: 卢万譞, 贾云得. 基于眼动数据的网络搜索行为预测方法[J]. 北京航空航天大学学报, 2015, 41(5): 904-910. doi: 10.13700/j.bh.1001-5965.2014.0464
LU Wanxuan, JIA Yunde. Predicting web search behavior based on gaze data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(5): 904-910. doi: 10.13700/j.bh.1001-5965.2014.0464(in Chinese)
Citation: LU Wanxuan, JIA Yunde. Predicting web search behavior based on gaze data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(5): 904-910. doi: 10.13700/j.bh.1001-5965.2014.0464(in Chinese)

基于眼动数据的网络搜索行为预测方法

doi: 10.13700/j.bh.1001-5965.2014.0464
基金项目: 高等学校博士学科点专项科研基金(20121101110035)
详细信息
    作者简介:

    卢万譞(1986—),男,北京人,博士研究生,luwanxuan@bit.edu.cn

    通讯作者:

    贾云得(1962—),男,山西大同人,教授,jiayunde@bit.edu.cn,主要研究方向为计算机视觉、人机交互和智能系统等.

  • 中图分类号: TP391

Predicting web search behavior based on gaze data

  • 摘要: 预测用户的网络搜索行为对改进搜索引擎和提升用户体验十分重要.现有大多数方法是基于用户的交互数据,如查询、点击和鼠标移动等.提出一种基于眼动数据的用户网络搜索行为预测方法.通过眼动实验,采集用户在网络搜索任务时的眼睛运动数据,将这些数据转化成两种数据格式:直方图和序列.直方图数据描述用户注意力的分布情况,序列数据呈现用户的扫视路径.使用4种学习算法对用户决策或用户意图进行预测,同时研究两种数据格式的性能.结果显示,两种数据格式均适合于预测用户决策,而序列数据更适合于预测用户意图.该结果表明,利用眼动数据能够有效预测网络搜索行为.

     

  • [1] Lee U,Liu Z,Cho J.Automatic identification of user goals in web search[C]//Proceedings of the 14th International Conference on World Wide Web.New York:ACM,2005:391-400.
    [2] Li X,Wang Y Y,Acero A.Learning query intent from regularized click graphs[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2008:339-346.
    [3] Shen Y,Yan J,Ji L,et al.Sparse hidden-dynamics conditional random fields for user intent understanding[C]//Proceedings of the 20th International Conference on World Wide Web.New York:ACM,2011:7-16.
    [4] Agichtein E,Brill E,Dumais S,et al.Learning user interaction models for predicting web search result preferences[C]//Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2006:3-10.
    [5] Hassan A,Jones R,Klinkner K L.Beyond dcg:user behavior as a predictor of a successful search[C]//Proceedings of the 3rd ACM International Conference on Web Search and Data Mining.New York:ACM,2010:211-230.
    [6] Moshfeghi Y,Jose J M.On cognition,emotion,and interaction aspects of search tasks with different search intentions[C]//Proceedings of the 22nd International Conference on World Wide Web.New York:ACM,2013:931-942.
    [7] Wang K,Gloy N,Li X. Inferring search behaviors using partially observable markov(pom) model[C]//Proceedings of the 3rd ACM International Conference on Web Search and Data Mining.New York:ACM,2010:211-220.
    [8] Guo Q,Agichtein E.Exploring mouse movements for inferring query intent[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2008:707-708.
    [9] Guo Q,Agichtein E.Ready to buy or just browsing :detecting web searcher goals from interaction data[C]//Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM,2010:130-137.
    [10] Arguello J.Predicting search task difficulty[C]//Proceedings of the 36th European Conference on IR Research.Switzerland:Springer International Publishing,2014:88-99.
    [11] Rodden K,Fu X,Aula A,et al.Eye-mouse coordination patterns on web search results pages[C]//Proceedings of the CHI'08 Extented Abstracts on Human Factors in Computing Systems.New York:ACM,2008:2997-3002.
    [12] Guo Q,Agichtein E.Towards predicting web searcher gaze position from mouse movements[C]//Proceedings of the CHI'10 Extended Abstracts on Human Factors in Computing Systems.New York:ACM,2010:3601-3606.
    [13] Faro A,Giordano D,Pino C,et al.Visual attention for implicit relevance feedback in a content based image retrieval[C]//Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications.New York:ACM,2010:73-76.
    [14] 施笑畏,黄瑶佳,胡鸿韬,等.眼动行为数据挖掘在提取网上购物决策因子中的应用[J].上海海事大学学报,2014,35(1): 60-64. Shi X W,Huang Y J,Hu H T,et al.Application of eye movement behavior data mining in identifying decision factors on online shopping[J].Journal of Shanghai Maritime University,2014,35(1):60-64(in Chinese).
    [15] Giordano D,Kavasidis I,Pino C,et al.Content based recommender systems by using eye gaze data[C]//Proceedings of the Symposium on Eye Tracking Research and Applications.New York:ACM,2012:369-372.
    [16] 秦林蝉,钟宁,吕胜富,等.一种融合视觉决策理论预测用户选择的方法[J].计算机应用研究,2013,30(8):2549-2551. Qin L C,Zhong N,Lv S F,et al.Visual decision making theory conbined method to predict user choice[J].Application Research of Computers,2013,30(8):2549-2551(in Chinese).
    [17] Bulling A,Weichel C,Gellersen H.Eyecontext:recognition of high-level contextual cues from human visual behavior[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.New York:ACM,2013:305-308.
    [18] Border A.A taxonomy of web search[C]//SIGIR Forum.New York:ACM,2002,36(2):3-10.
    [19] Chang C C,Lin C J.Libsvm:a library for support vector machines[J].ACM Transactions on Intelligent System and Technology,2011,2(3):27.
    [20] Breiman L.Random forests[J].Machine Learning,2001,45(1): 5-32.
    [21] Murphy K.Hidden Markov model(HMM)toolbox for Matlab[CP/OL].[2014-04-20].http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html.
    [22] Morency L P,Christoudias C M,Quattoni A,et al.HCRF 2.0b[CP/OL].[2014-04-20].http://sourceforge.net/projects/hcrf.

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出版历程
  • 收稿日期:  2014-04-28
  • 修回日期:  2014-08-01
  • 网络出版日期:  2015-05-20

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