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Effects of Position and Time Bias on Understanding Onsite Users' Behavior

Published: 13 March 2016 Publication History

Abstract

The existence of different biases in logged users' behavior makes it difficult to extract realistic topical and social information from users' interaction logs (e.g., query logs). To understand users' behavior and their interests in the cultural heritage domain, we have logged onsite user interaction logs of visits in a museum. This prompts the question on the reliability of the social information being gathered from the onsite logs: How does the position of museum objects affect users' behavior in the museum? How does order of visiting point of interests affect their dwell-time in front of each point of interest? How do different users' characteristics affect their behavior in the museum? In short, what are different kinds of biases that should be considered in the onsite logs? Our main findings are the following: First, there is a considerable position bias, which is due to the design of the exhibition and should be considered during extraction of social signals from the log. Second, there is a bias in the amount of time that users spend for interacting with the point of interests and the order of picking them to visit. This shows a fatigue on users' interactions while they are reaching to the end of the exhibition. Third, we find out some variations among the users' visit, which shows context is an important factor to consider while using onsite logs for different purposes.

References

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N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An Experimental Comparison of Click Position-bias Models. In WSDM, pages 87--94, 2008.
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L. A. Granka, T. Joachims, and G. Gay. Eye-tracking Analysis of User Behavior in WWW Search. In SIGIR, pages 478--479, 2004.
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S. H. Hashemi, C. L. Clarke, A. Dean-Hall, J. Kamps, and J. Kiseleva. On the Reusability of Open Test Collections. In SIGIR, pages 827--830, 2015.
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T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay.Accurately Interpreting Clickthrough Data As Implicit Feedback. In SIGIR, pages 154--161, 2005.
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Y. Zhang, W. Chen, D. Wang, and Q. Yang. User-click Modeling for Understanding and Predicting Search-behavior. In KDD, pages 1388--1396, 2011.

Cited By

View all
  • (2021)ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on DisplaySensors10.3390/s2104152721:4(1527)Online publication date: 22-Feb-2021
  • (2021)Understanding IIIF image usage based on server log analysisDigital Scholarship in the Humanities10.1093/llc/fqab04036:Supplement_2(ii210-ii221)Online publication date: 1-Jun-2021
  • (2021)exhiSTORY: Smart Self-organizing ExhibitsBig Data Platforms and Applications10.1007/978-3-030-38836-2_5(91-111)Online publication date: 29-Sep-2021
  • Show More Cited By

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cover image ACM Conferences
CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval
March 2016
400 pages
ISBN:9781450337519
DOI:10.1145/2854946
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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2016

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Author Tags

  1. behavioral dynamics
  2. bias
  3. internet of things
  4. onsite logs

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  • Short-paper

Funding Sources

  • EU project, Material EncounterS with digital Cultural Heritage (MEsCH, European Community's 7th Framework Program on ICT for access to cultural resources

Conference

CHIIR '16
Sponsor:
CHIIR '16: Conference on Human Information Interaction and Retrieval
March 13 - 17, 2016
North Carolina, Carrboro, USA

Acceptance Rates

CHIIR '16 Paper Acceptance Rate 23 of 58 submissions, 40%;
Overall Acceptance Rate 55 of 163 submissions, 34%

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Cited By

View all
  • (2021)ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on DisplaySensors10.3390/s2104152721:4(1527)Online publication date: 22-Feb-2021
  • (2021)Understanding IIIF image usage based on server log analysisDigital Scholarship in the Humanities10.1093/llc/fqab04036:Supplement_2(ii210-ii221)Online publication date: 1-Jun-2021
  • (2021)exhiSTORY: Smart Self-organizing ExhibitsBig Data Platforms and Applications10.1007/978-3-030-38836-2_5(91-111)Online publication date: 29-Sep-2021
  • (2019)Location-based personalized recommender system in the internet of cultural thingsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-169973(1-12)Online publication date: 17-Jan-2019
  • (2018)Interacting with Heritage: On the Use and Potential of IoT Within the Cultural Heritage Sector2018 Fifth International Conference on Internet of Things: Systems, Management and Security10.1109/IoTSMS.2018.8554899(15-22)Online publication date: Oct-2018
  • (2018)exhiSTORYFuture Generation Computer Systems10.1016/j.future.2017.10.03881:C(542-556)Online publication date: 1-Apr-2018
  • (2017)Busy versus Empty MuseumsAdjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3099023.3099088(333-334)Online publication date: 9-Jul-2017
  • (2017)Where To Go Next?Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3079628.3079687(50-58)Online publication date: 9-Jul-2017
  • (2017)Skip or StayProceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval10.1145/3020165.3022160(389-392)Online publication date: 7-Mar-2017

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