Personality, User Preferences and Behavior in Recommender systems.

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Title: Personality, User Preferences and Behavior in Recommender systems.
Authors: Karumur, Raghav Pavan1 raghav@umn.edu, Nguyen, Tien T.1, Konstan, Joseph A.1
Source: Information Systems Frontiers. Dec2018, Vol. 20 Issue 6, p1241-1265. 25p. 1 Diagram, 12 Charts, 1 Graph.
Subjects: Computer user attitudes, Personality assessment, Consumer behavior, Consumer preferences, Customer retention
Abstract: This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns. [ABSTRACT FROM AUTHOR]
Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Personality, User Preferences and Behavior in Recommender systems.
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  Data: <searchLink fieldCode="AR" term="%22Karumur%2C+Raghav+Pavan%22">Karumur, Raghav Pavan</searchLink><relatesTo>1</relatesTo><i> raghav@umn.edu</i><br /><searchLink fieldCode="AR" term="%22Nguyen%2C+Tien+T%2E%22">Nguyen, Tien T.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Konstan%2C+Joseph+A%2E%22">Konstan, Joseph A.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Information+Systems+Frontiers%22">Information Systems Frontiers</searchLink>. Dec2018, Vol. 20 Issue 6, p1241-1265. 25p. 1 Diagram, 12 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Computer+user+attitudes%22">Computer user attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Personality+assessment%22">Personality assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+behavior%22">Consumer behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+preferences%22">Consumer preferences</searchLink><br /><searchLink fieldCode="DE" term="%22Customer+retention%22">Customer retention</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10796-017-9800-0
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      – Code: eng
        Text: English
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      – SubjectFull: Computer user attitudes
        Type: general
      – SubjectFull: Personality assessment
        Type: general
      – SubjectFull: Consumer behavior
        Type: general
      – SubjectFull: Consumer preferences
        Type: general
      – SubjectFull: Customer retention
        Type: general
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      – TitleFull: Personality, User Preferences and Behavior in Recommender systems.
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            NameFull: Karumur, Raghav Pavan
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              Text: Dec2018
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              Y: 2018
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