Predicting user behavior in electronic markets based on personality-mining in large online social networks A personality-based product recommender framework

• Published in: Electronic markets Vol. 27; no. 3; pp. 247 - 265
• Main Author: Buettner, Ricardo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017; Springer Nature B.V
• Subjects: Business and Management; Digital media; e-Commerce/e-business; Electronic commerce; Information technology; IT in Business; Markets; Mining; Personality; Predictive analytics; Preferences; Recommender systems; Research Paper; Social media; Social networks; System effectiveness; User behavior; Neuroticism; Conscientiousness; C53; D40; C55; Product recommender system; Big data analytics; M31; Predictive analytics; Agreeableness; M37; Extraversion; Openness to experience; Machine learning; Personality mining; C90; Five factor model; C81; Online social networks
• ISSN: 1019-6781; 1422-8890
• DOI: 10.1007/s12525-016-0228-z

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.