Xiaohui Xie
Yiqun Liu
Meng Wang
Min Zhang
ABSTRACT
Image search engines show results differently from general Web search engines in three key ways: (1) most Web-based image search engines adopt the two-dimensional result placement instead of the linear result list; (2) image searches show snapshots instead of snippets (query-dependent abstracts of landing pages) on search engine result pages (SERPs); and (3) pagination is usually not (explicitly) supported on image search SERPs, and users can view results without having to click on the "next page'' button. Compared with the extensive study of user behavior in general Web search scenarios, there exists no thorough investigation how the different interaction mechanism of image search engines affects users' examination behavior. To shed light on this research question, we conducted an eye-tracking study to investigate users' examination behavior in image searches. We focus on the impacts of factors in examination including position, visual saliency, edge density, the existence of textual information, and human faces in result images. Three interesting findings indicate users' behavior biases: (1) instead of the traditional "Golden Triangle'' phenomena in the user examination patterns of general Web search, we observe a middle-position bias, (2) besides the position factor, the content of image results (e.g., visual saliency) affects examination behavior, and (3) some popular behavior assumptions in general Web search (e.g., examination hypothesis) do not hold in image search scenarios. We predict users' examination behavior with different impact factors. Results show that combining position and visual content features can improve prediction in image searches.
- Eugene Agichtein, Eric Brill, and Susan Dumais. 2006. Improving web search ranking by incorporating user behavior information Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 19--26.Google Scholar
- Paul André, Edward Cutrell, Desney S. Tan, and Greg Smith 2009. Designing novel image search interfaces by understanding unique characteristics and usage IFIP Conference on Human-Computer Interaction. Springer, 340--353.Google Scholar
- Ioannis Arapakis and Luis A. Leiva 2016. Predicting user engagement with direct displays using mouse cursor information Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 599--608.Google Scholar
- Roland J. Baddeley and Benjamin W. Tatler 2006. High frequency edges (but not contrast) predict where we fixate: A Bayesian system identification analysis. Vision research, Vol. 46, 18 (2006), 2824--2833. Google ScholarCross Ref
- Georg Buscher, Edward Cutrell, and Meredith Ringel Morris. 2009. What do you see when you're surfing? Using eye tracking to predict salient regions of web pages. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 21--30.Google ScholarDigital Library
- John Canny. 1986. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence 6 (1986), 679--698. Google ScholarDigital Library
- Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, and Hang Li 2008. Context-aware query suggestion by mining click-through and session data Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 875--883.Google Scholar
- Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey 2008. An experimental comparison of click position-bias models Proceedings of the 2008 International Conference on Web Search and Data Mining. ACM, 87--94.Google Scholar
- Edward Cutrell and Zhiwei Guan 2007. What are you looking for? an eye-tracking study of information usage in web search Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 407--416.Google Scholar
- Georges E. Dupret and Benjamin Piwowarski 2008. A user browsing model to predict search engine click data from past observations. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 331--338. Google ScholarDigital Library
- Abby Goodrum and Amanda Spink 1999. Visual Information Seeking: A Study of Image Queries on the World Wide Web. Proceedings of the ASIS Annual Meeting, Vol. Vol. 36. ERIC, 665--74.Google Scholar
- Jonathan Harel, Christof Koch, and Pietro Perona. 2006. Graph-based visual saliency. In Advances in neural information processing systems. 545--552.Google Scholar
- Gord Hotchkiss, Steve Alston, and Greg Edwards. 2005. Eye tracking study. Research white paper, Enquiro Search Solutions Inc (2005).Google Scholar
- Xian-Sheng Hua, Linjun Yang, Jingdong Wang, Jing Wang, Ming Ye, Kuansan Wang, Yong Rui, and Jin Li. 2013. Clickage: towards bridging semantic and intent gaps via mining click logs of search engines Proceedings of the 21st ACM international conference on Multimedia. ACM, 243--252.Google Scholar
- Vidit Jain and Manik Varma 2011. Learning to re-rank: query-dependent image re-ranking using click data Proceedings of the 20th international conference on World wide web. ACM, 277--286.Google Scholar
- Jyun-Yu Jiang, Yen-Yu Ke, Pao-Yu Chien, and Pu-Jen Cheng. 2014. Learning user reformulation behavior for query auto-completion Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 445--454.Google Scholar
- Yanen Li, Anlei Dong, Hongning Wang, Hongbo Deng, Yi Chang, and ChengXiang Zhai. 2014. A two-dimensional click model for query auto-completion. Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 455--464. Google ScholarDigital Library
- Yiqun Liu, Zeyang Liu, Ke Zhou, Meng Wang, Huanbo Luan, Chao Wang, Min Zhang, and Shaoping Ma. 2016. Predicting Search User Examination with Visual Saliency Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 619--628.Google Scholar
- Yiqun Liu, Chao Wang, Ke Zhou, Jianyun Nie, Min Zhang, and Shaoping Ma. 2014. From skimming to reading: A two-stage examination model for web search Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 849--858.Google Scholar
- Neil O'Hare, Paloma de Juan, Rossano Schifanella, Yunlong He, Dawei Yin, and Yi Chang. 2016. Leveraging user interaction signals for web image search Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 559--568.Google Scholar
- Bing Pan, Helene A. Hembrooke, Geri K. Gay, Laura A. Granka, Matthew K. Feusner, and Jill K. Newman. 2004. The determinants of web page viewing behavior: an eye-tracking study Proceedings of the 2004 symposium on Eye tracking research & applications. ACM, 147--154.Google Scholar
- Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, and Yong Rui. 2014. Click-through-based cross-view learning for image search Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 717--726.Google Scholar
- Jaimie Y. Park, Neil O'Hare, Rossano Schifanella, Alejandro Jaimes, and Chin-Wan Chung. 2015. A large-scale study of user image search behavior on the web Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 985--994.Google Scholar
- Hsiao-Tieh Pu. 2005. A comparative analysis of web image and textual queries. Online Information Review Vol. 29, 5 (2005), 457--467. Google ScholarCross Ref
- Stephen W. Raudenbush and Anthony S. Bryk 2002. Hierarchical linear models: Applications and data analysis methods. Vol. Vol. 1.Google Scholar
- Sara Shatford. 1986. Analyzing the subject of a picture: a theoretical approach. Cataloging & classification quarterly Vol. 6, 3 (1986), 39--62. Google ScholarCross Ref
- Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta, and Ramesh Jain. 2000. Content-based image retrieval at the end of the early years. IEEE Transactions on pattern analysis and machine intelligence, Vol. 22, 12 (2000), 1349--1380. Google ScholarDigital Library
- Gavin Smith and Helen Ashman 2009. Evaluating implicit judgements from image search interactions. (2009).Google Scholar
- Benjamin W. Tatler and Benjamin T. Vincent 2009. The prominence of behavioural biases in eye guidance. Visual Cognition, Vol. 17, 6-7 (2009), 1029--1054.Google ScholarCross Ref
- Dian Tjondronegoro, Amanda Spink, and Bernard J. Jansen. 2009. A study and comparison of multimedia Web searching: 1997--2006. Journal of the American Society for Information Science and Technology, Vol. 60, 9 (2009), 1756--1768. Google ScholarDigital Library
- Geoffrey Underwood and Tom Foulsham 2006. Visual saliency and semantic incongruency influence eye movements when inspecting pictures. The Quarterly journal of experimental psychology, Vol. 59, 11 (2006), 1931--1949. Google ScholarCross Ref
- Chao Wang, Yiqun Liu, Meng Wang, Ke Zhou, Jian-yun Nie, and Shaoping Ma. 2015. Incorporating non-sequential behavior into click models Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 283--292.Google Scholar
- Kuansan Wang, Nikolas Gloy, and Xiaolong Li 2010. Inferring search behaviors using partially observable Markov (POM) model Proceedings of the third ACM international conference on Web search and data mining. ACM, 211--220.Google Scholar
- Yue Wang, Dawei Yin, Luo Jie, Pengyuan Wang, Makoto Yamada, Yi Chang, and Qiaozhu Mei 2016. Beyond ranking: Optimizing whole-page presentation Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 103--112.Google Scholar
- Wei Wu, Hang Li, and Jun Xu 2013. Learning query and document similarities from click-through bipartite graph with metadata Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 687--696.Google Scholar
- Danqing Xu, Yiqun Liu, Min Zhang, Shaoping Ma, and Liyun Ru 2012. Incorporating revisiting behaviors into click models Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 303--312.Google Scholar
- Yongdong Zhang, Xiaopeng Yang, and Tao Mei 2014. Image search reranking with query-dependent click-based relevance feedback. IEEE transactions on image processing Vol. 23, 10 (2014), 4448--4459. Google ScholarCross Ref
Index Terms
- Investigating Examination Behavior of Image Search Users
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