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Putting the person back into personalization

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Elizabeth F. ChurchillAuthors Info & Claims

Interactions, Volume 20, Issue 5

Pages 12 - 15

https://doi.org/10.1145/2504847

Published: 01 September 2013 Publication History

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References

[1]

http://dictionary.reference.com/browse/personalization

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[2]

Chamberlin, E. Theory of Monopolistic Competition. Harvard University Press, Cambridge, MA, 1933.

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[3]

Robinson, H.W. The equilibrium price in a perfect inter-temporal market. Econometrica 6, 1 (1938), 48--62.

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[4]

Smith, W. Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing 21, 1 (1956), 3--8.

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[5]

I have borrowed and extended the distinction between outcome and process personalization from some very nice work on face-to-face service encounters: Surprenant, C.F. and Solomon, M.R. Personalization in the service encounter. Journal of Marketing 51, 2 (Apr. 1987), 86--96; http://www.ida.liu.se/~steho/und/htdd01/4999772.pdf

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[6]

Pariser, Eli. The Filter Bubble: What the Internet Is Hiding from You. Viking/Penguin Press, London, 2011. See also Bosker, B. Tim Berners-Lee: Facebook threatens Web, beware. The Guardian (Nov. 22, 2010); http://www.guardian.co.uk/technology/2010/nov/22/tim-berners-lee-facebook

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Google Scholar

[7]

Krug, S. Don't Make Me Think. A Common Sense Approach to Web Usability. New Riders, 2005.

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Google Scholar

[8]

Christensen, C. The Innovator's Dilemma: The Revolutionary Book That Will Change the Way You Do Business. Harper Business, 2011.

Google Scholar

[9]

Bucket testing or A/B testing is "a methodology in advertising of using randomized experiments with two variants, A and B, which are the Control and Treatment in the controlled experiment. Such experiments are commonly used in web development and marketing, as well as in more traditional forms of advertising. Other names include randomized controlled experiments, online controlled experiments, and split testing." See http://en.wikipedia.org/wiki/A/B_testing

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Moloto A(2024)The Impact of Poor Market Segmentation on the Survival and Growth of Small Livestock Farmers in Gert Sibande MunicipalityJournal of Public Administration and Development Alternatives10.55190/JPADA.2024.3219:1(164-178)Online publication date: Jul-2024
https://doi.org/10.55190/JPADA.2024.321
van Renswouw LBogers SLallemand Cvan Wesemael PVos S(2024)Changing Perspective on Data in Designing for Active EnvironmentsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661635(2403-2419)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661635
Greene TShmueli G(2023)Persons and Personalization on Digital PlatformsPhilosophy of Artificial Intelligence and Its Place in Society10.4018/978-1-6684-9591-9.ch011(214-270)Online publication date: 16-Oct-2023
https://doi.org/10.4018/978-1-6684-9591-9.ch011
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Index Terms

Putting the person back into personalization
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Multi / mixed media creation
  2. Law, social and behavioral sciences
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Hypertext / hypermedia

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Published In

Interactions Volume 20, Issue 5

September + October 2013

81 pages

ISSN:1072-5520

EISSN:1558-3449

DOI:10.1145/2512050

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Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 01 September 2013

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Moloto A(2024)The Impact of Poor Market Segmentation on the Survival and Growth of Small Livestock Farmers in Gert Sibande MunicipalityJournal of Public Administration and Development Alternatives10.55190/JPADA.2024.3219:1(164-178)Online publication date: Jul-2024
https://doi.org/10.55190/JPADA.2024.321
van Renswouw LBogers SLallemand Cvan Wesemael PVos S(2024)Changing Perspective on Data in Designing for Active EnvironmentsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661635(2403-2419)Online publication date: 1-Jul-2024
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