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A Two-Stage Model for User's Examination Behavior in Mobile Search

Published: 01 March 2018 Publication History

Abstract

With the rapid growth of mobile search, it is important to understand how users browse the mobile SERPs and allocate their limited attention to each result. To address this problem, we introduce a two-stage examination model that can separately capture the position bias with a skimming model and the attractiveness bias with an attractiveness model. The effectiveness of the proposed model is validated by using a dataset that contains explicit examination feedbacks from users. We further investigate user»s examination behaviors by analyzing the model parameters learned via EM algorithm. The results reveal some interesting findings such as how the skimming behavior is dependent on the previous examination sequence and what factors are associated with the attractiveness of search results on mobile SERPs.

References

[1]
Pia Borlund . 2000. Experimental components for the evaluation of interactive information retrieval systems. Journal of documentation Vol. 56, 1 (2000), 71--90.
[2]
John Canny . 1986. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence 6 (1986), 679--698.
[3]
Georges E Dupret and Benjamin Piwowarski . 2008. A user browsing model to predict search engine click data from past observations. SIGIR'08. ACM, 331--338.
[4]
Jonathan Harel, Christof Koch, and Pietro Perona . 2007. Graph-based visual saliency. In Advances in neural information processing systems. 545--552.
[5]
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay . 2005. Accurately interpreting clickthrough data as implicit feedback SIGIR'05. Acm, 154--161.
[6]
Jaewon Kim, Paul Thomas, Ramesh Sankaranarayana, Tom Gedeon, and Hwan-Jin Yoon . 2016. Understanding eye movements on mobile devices for better presentation of search results. JASIST, Vol. 67, 11 (2016), 2607--2619.
[7]
Dmitry Lagun, Chih-Hung Hsieh, Dale Webster, and Vidhya Navalpakkam . 2014. Towards better measurement of attention and satisfaction in mobile search SIGIR'14. ACM, 113--122.
[8]
Dmitry Lagun, Donal McMahon, and Vidhya Navalpakkam . 2016. Understanding mobile searcher attention with rich ad formats CIKM'16. ACM, 599--608.
[9]
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 SIGIR'16. ACM, 619--628.
[10]
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 CIKM'14. ACM, 849--858.
[11]
Zeyang Liu, Yiqun Liu, Ke Zhou, Min Zhang, and Shaoping Ma . 2015. Influence of vertical result in web search examination SIGIR'15. ACM, 193--202.
[12]
Cheng Luo, Yiqun Liu, Tetsuya Sakai, Fan Zhang, Min Zhang, and Shaoping Ma . 2017. Evaluating Mobile Search with Height-Biased Gain. SIGIR'17. ACM.
[13]
Matthew Richardson, Ewa Dominowska, and Robert Ragno . 2007. Predicting clicks: estimating the click-through rate for new ads WWW'07. ACM, 521--530.
[14]
Kyle Williams, Julia Kiseleva, Aidan C Crook, Imed Zitouni, Ahmed Hassan Awadallah, and Madian Khabsa . 2016. Detecting good abandonment in mobile search. In WWW'16. 495--505.

Cited By

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  • (2020)A Price-per-attention Auction Scheme Using Mouse Cursor InformationACM Transactions on Information Systems10.1145/337421038:2(1-30)Online publication date: 27-Jan-2020

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cover image ACM Conferences
CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval
March 2018
402 pages
ISBN:9781450349253
DOI:10.1145/3176349
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 01 March 2018

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Author Tags

  1. examination
  2. mobile search
  3. selective attention
  4. user behavior analysis

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CHIIR '18
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CHIIR '18 Paper Acceptance Rate 22 of 57 submissions, 39%;
Overall Acceptance Rate 55 of 163 submissions, 34%

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  • (2020)A Price-per-attention Auction Scheme Using Mouse Cursor InformationACM Transactions on Information Systems10.1145/337421038:2(1-30)Online publication date: 27-Jan-2020

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