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Personalization using Big Data Analytics Platforms

Published: 20 July 2016 Publication History

Abstract

Personalization can be defined as the customization of the outputs of a system based on the collected personal information of its users. Personalization techniques rely on user information, such as interests, preferences, geographic location, etc. The data being collected is used to create a profile, and improve the relevance of the outputs presented to the user. Google's search engine, or Facebook's suggestions are examples of personalization. This paper intent to provide an overview of the concept, and pretend to answer the question: Which level of personalization can Big Data Analytics Platforms support?

References

[1]
L. Bustos, 2013. Using Big Data for Big Personalization {infographic}, no -- July 2013, Available: http://www.getelastic.com/using-big-data-for-big-personalization-infographic/.
[2]
Habegger, B., Hasan, O., Brunie, L., Bennani, N., Kosch, H., & Damiani, E. (2014). Personalization vs. privacy in big data analysis. International Journal of Big Data, 1(1), 25--35.
[3]
Neves P., Bernardino J. 2015. Big Data Issues. In Proceedings of the 19th International Database Engineering & Applications Symposium (IDEAS '15). ACM, New York, NY, USA, 200--201.
[4]
A. Lella, 2016, comScore Releases February 2016 U.S. Desktop Search Engine Ranking, March, 2016.
[5]
IBM Knowledge Center. https://www.ibm.com/support/knowledgecenter/
[6]
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
[7]
L. Person, 2014, World Hadoop Market -- Opportunities and Forecasts 2020, Allied Market Research.
[8]
L. Kart, G. Herschel, A. Linden, J. Hare, 2016, Gartner 2016 Magic Quadrant for Advanced Analytics Platforms, Gartner.
[9]
Martineau K, Location Data on Two Apps Enough to Identify Someone, Says Study, Columbia University, 2016.

Cited By

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  • (2024)AI-Personalization ParadoxAI Impacts in Digital Consumer Behavior10.4018/979-8-3693-1918-5.ch004(82-111)Online publication date: 1-Mar-2024
  • (2024)Understanding customer behavior by mapping complaints to personality based on social media textual dataData Technologies and Applications10.1108/DTA-02-2024-016259:1(155-179)Online publication date: 9-Sep-2024
  • (2022)Using Dynamic Pruned N-Gram Model for Identifying the Gender of the UserApplied Sciences10.3390/app1213637812:13(6378)Online publication date: 23-Jun-2022
  • Show More Cited By

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Published In

cover image ACM Other conferences
C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering
July 2016
152 pages
ISBN:9781450340755
DOI:10.1145/2948992
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

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Author Tags

  1. Big Data
  2. Big Data Analytics Platforms
  3. Personalization

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  • Refereed limited

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C3S2E '16

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Overall Acceptance Rate 12 of 42 submissions, 29%

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Cited By

View all
  • (2024)AI-Personalization ParadoxAI Impacts in Digital Consumer Behavior10.4018/979-8-3693-1918-5.ch004(82-111)Online publication date: 1-Mar-2024
  • (2024)Understanding customer behavior by mapping complaints to personality based on social media textual dataData Technologies and Applications10.1108/DTA-02-2024-016259:1(155-179)Online publication date: 9-Sep-2024
  • (2022)Using Dynamic Pruned N-Gram Model for Identifying the Gender of the UserApplied Sciences10.3390/app1213637812:13(6378)Online publication date: 23-Jun-2022
  • (2021)Adding Personal Touches to IoTBig Data Analytics for Internet of Things10.1002/9781119740780.ch6(167-186)Online publication date: 2-Apr-2021

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