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

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  • Ricardo Buettner

    (FOM University of Applied Sciences)

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.

Suggested Citation

  • Ricardo Buettner, 2017. "Predicting user behavior in electronic markets based on personality-mining in large online social networks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 247-265, August.
    • Handle: RePEc:spr:elmark:v:27:y:2017:i:3:d:10.1007_s12525-016-0228-z
    DOI: 10.1007/s12525-016-0228-z
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    References listed on IDEAS

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    Cited by:

    1. Annette Wenninger & Daniel Rau & Maximilian Röglinger, 2022. "Improving customer satisfaction in proactive service design," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1399-1418, September.
    2. Tamilla Triantoro & Ram Gopal & Raquel Benbunan-Fich & Guido Lang, 2020. "Personality and games: enhancing online surveys through gamification," Information Technology and Management, Springer, vol. 21(3), pages 169-178, September.
    3. Ponce, Pedro & Peffer, Therese & Molina, Arturo & Barcena, Sergio, 2020. "Social creation networks for designing low income interfaces in programmable thermostats," Technology in Society, Elsevier, vol. 62(C).
    4. Argilés-Bosch, Josep M. & Somoza, Antonio & Ravenda, Diego & García-Blandón, Josep, 2020. "An empirical examination of the influence of e-commerce on tax avoidance in Europe," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 41(C).
    5. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    6. Vamsi Vallurupalli & Indranil Bose, 2020. "Exploring thematic composition of online reviews: A topic modeling approach," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 791-804, December.
    7. Luk Luk Fuadah & Kencana Dewi & Mukhtaruddin Mukhtaruddin & Umi Kalsum & Anton Arisman, 2022. "The Relationship between Sustainability Reporting, E-Commerce, Firm Performance and Tax Avoidance with Organizational Culture as Moderating Variable in Small and Medium Enterprises in Palembang," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    8. Rainer Alt & Hans-Dieter Zimmermann, 2019. "Electronic Markets on platform competition," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 143-149, June.
    9. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    10. Tamilla Triantoro & Ram Gopal & Raquel Benbunan-Fich & Guido Lang, 0. "Personality and games: enhancing online surveys through gamification," Information Technology and Management, Springer, vol. 0, pages 1-10.
    11. Zofia Saternus & Patrick Weber & Oliver Hinz, 2022. "The effects of advertisement disclosure on heavy and light Instagram users," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1351-1372, September.

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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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