Personas for Content Creators via Decomposed Aggregate Audience Statistics

Personas for Content Creators via Decomposed Aggregate Audience Statistics

We propose a novel method for generating personas based on online user data for the increasingly common situation of content creators distributing products via online platforms. We use non-negative matrix factorization to identify user segments and develop personas by adding personality such as name...

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Main Authors: AN, Jisun, KWAK, Haewoon, JANSEN, Bernard J.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Artificial Intelligence and Robotics
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/6290
https://ink.library.smu.edu.sg/context/sis_research/article/7293/viewcontent/Personas_for_Content_Creators_via_Decomposed_Aggregate_Audience_Statistics.pdf
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-72932021-11-23T07:52:32Z Personas for Content Creators via Decomposed Aggregate Audience Statistics AN, Jisun KWAK, Haewoon JANSEN, Bernard J. We propose a novel method for generating personas based on online user data for the increasingly common situation of content creators distributing products via online platforms. We use non-negative matrix factorization to identify user segments and develop personas by adding personality such as names and photos. Our approach can develop accurate personas representing real groups of people using online user data, versus relying on manually gathered data. 2017-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6290 info:doi/10.1145/3110025.3110072 https://ink.library.smu.edu.sg/context/sis_research/article/7293/viewcontent/Personas_for_Content_Creators_via_Decomposed_Aggregate_Audience_Statistics.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Numerical Analysis and Scientific Computing
spellingShingle Artificial Intelligence and Robotics
Numerical Analysis and Scientific Computing
AN, Jisun
KWAK, Haewoon
JANSEN, Bernard J.
Personas for Content Creators via Decomposed Aggregate Audience Statistics
description We propose a novel method for generating personas based on online user data for the increasingly common situation of content creators distributing products via online platforms. We use non-negative matrix factorization to identify user segments and develop personas by adding personality such as names and photos. Our approach can develop accurate personas representing real groups of people using online user data, versus relying on manually gathered data.
format text
author AN, Jisun
KWAK, Haewoon
JANSEN, Bernard J.
author_facet AN, Jisun
KWAK, Haewoon
JANSEN, Bernard J.
author_sort AN, Jisun
title Personas for Content Creators via Decomposed Aggregate Audience Statistics
title_short Personas for Content Creators via Decomposed Aggregate Audience Statistics
title_full Personas for Content Creators via Decomposed Aggregate Audience Statistics
title_fullStr Personas for Content Creators via Decomposed Aggregate Audience Statistics
title_full_unstemmed Personas for Content Creators via Decomposed Aggregate Audience Statistics
title_sort personas for content creators via decomposed aggregate audience statistics
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/6290
https://ink.library.smu.edu.sg/context/sis_research/article/7293/viewcontent/Personas_for_Content_Creators_via_Decomposed_Aggregate_Audience_Statistics.pdf
_version_ 1770575928709087232

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