Personality and Recommender Systems

Marko Tkalčič*, Li Chen

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

3 Citations (Scopus)

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 generating diverse recommendations. However, a number of challenges still remain.

Original languageEnglish
Title of host publicationRecommender Systems Handbook
EditorsFrancesco Ricci, Lior Rokach, Bracha Shapira
Place of PublicationNew York
PublisherSpringer
Pages757-787
Number of pages31
Edition3rd
ISBN (Electronic)9781071621974
ISBN (Print)9781071621967, 9781071621998
DOIs
Publication statusPublished - 21 Apr 2022

Scopus Subject Areas

  • Computer Science(all)

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