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

Ricardo Buettner ()
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Ricardo Buettner: FOM University of Applied Sciences

Electronic Markets, 2017, vol. 27, issue 3, No 7, 247-265

Abstract: 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.

Keywords: Big data analytics; Predictive analytics; Online social networks; Machine learning; Product recommender system; Personality mining; Five factor model; Extraversion; Neuroticism; Openness to experience; Conscientiousness; Agreeableness (search for similar items in EconPapers)
JEL-codes: C53 C55 C81 C90 D40 M31 M37 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s12525-016-0228-z

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