Predicting user behavior in electronic markets based on personality-mining in large online social networks

Predicting user behavior in electronic markets based on personality-mining in large online social... Determining a user’s preferences is an important condition for effectively operating automatic recommendation systems. Since personality theory claims that a user’s personality substantially influences preference, I propose a personality-based product recommender (PBPR) framework to analyze social media data in order to predict a user’s personality and to subsequently derive its personality-based product preferences. The PBRS framework will be evaluated as an IT-artefact with a unique online social network XING dataset and a unique coffeemaker preference dataset. My evaluation results show (a) the possibility of predicting a user’s personality from social media data, as I reached a predictive gain between 23.2 and 41.8 percent and (b) the possibility of recommending products based on a user’s personality, as I reached a predictive gain of 45.1 percent. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Electronic Markets Springer Journals

Predicting user behavior in electronic markets based on personality-mining in large online social networks

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by The Author(s)
Subject
Business and Management; IT in Business; e-Commerce/e-business
ISSN
1019-6781
eISSN
1422-8890
D.O.I.
10.1007/s12525-016-0228-z
Publisher site
See Article on Publisher Site

Abstract

Determining a user’s preferences is an important condition for effectively operating automatic recommendation systems. Since personality theory claims that a user’s personality substantially influences preference, I propose a personality-based product recommender (PBPR) framework to analyze social media data in order to predict a user’s personality and to subsequently derive its personality-based product preferences. The PBRS framework will be evaluated as an IT-artefact with a unique online social network XING dataset and a unique coffeemaker preference dataset. My evaluation results show (a) the possibility of predicting a user’s personality from social media data, as I reached a predictive gain between 23.2 and 41.8 percent and (b) the possibility of recommending products based on a user’s personality, as I reached a predictive gain of 45.1 percent.

Journal

Electronic MarketsSpringer Journals

Published: Jul 7, 2016

References

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