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Recommender Systems Handbook pp 715–739Cite as

Personality and Recommender Systems

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Abstract

Personality, as defined in psychology, accounts for the individual differences in users’ preferences and behaviour. It has been found that there are significant correlations between personality and users’ characteristics that are traditionally used by recommender systems (e.g. music preferences, social media behaviour, learning styles etc.). Among the many models of personality, the Five Factor Model (FFM) appears suitable for usage in recommender systems as it can be quantitatively measured (i.e. numerical values for each of the factors, namely, openness, conscientiousness, extraversion, agreeableness and neuroticism). The acquisition of the personality factors for an observed user can be done explicitly through questionnaires or implicitly using machine learning techniques with features extracted from social media streams or mobile phone call logs. There are, although limited, a number of available datasets to use in offline recommender systems experiment. Studies have shown that personality was successful at tackling the cold-start problem, making group recommendations, addressing cross-domain preferences and at generating diverse recommendations. However, a number of challenges still remain.

Keywords

  • Recommender System
  • Five Factor Model
  • Personality Parameter
  • Music Preference
  • International Personality Item Pool

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Notes

  1. 1.

    The Thomas-Kilman conflict mode instrument is available at http://cmpresolutions.co.uk/wp-content/uploads/2011/04/Thomas-Kilman-conflict-instrument-questionaire.pdf.

  2. 2.

    http://ipip.ori.org/.

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Acknowledgements

Part of the work presented in this chapter has received funding from the European Union FP7 programme through the PHENICX project (grant agreement no. 601166), China National Natural Science Foundation (no. 61272365), and Hong Kong Research Grants Council (no. ECS/HKBU211912).

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  1. Johannes Kepler University, Altenberger Strasse 69, Linz, Austria

    Marko Tkalcic

  2. Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, China

    Li Chen

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Tkalcic, M., Chen, L. (2015). Personality and Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_21

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