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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

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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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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