On the Exploitation of User Personality in Recommender Systems

In this paper we revise the state of the art on personality-aware recommender systems, identifying main research trends and achievements up to date, and discussing open issues that may be addressed in the future.

[1]  Carla H. Lagorio,et al.  Psychology , 1929, Nature.

[2]  P. Costa,et al.  Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five-Factor Inventory (NEO-FFI) , 1992 .

[3]  R. R. Abidin.  Psychological Assessment Resources , 1995 .

[4]  D. Rawlings,et al.  Music Preference and the Five-Factor Model of the NEO Personality Inventory , 1997 .

[5]  L. R. Goldberg.  A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models , 1999 .

[6]  Dennis McLeod,et al.  Exploiting and Learning Human Temperaments for Customized Information Recommendation , 2002, IMSA.

[7]  S. Gosling,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES The Do Re Mi’s of Everyday Life: The Structure and Personality Correlates of Music Preferences , 2003 .

[8]  John A. Johnson,et al.  The international personality item pool and the future of public-domain personality measures ☆ , 2006 .

[9]  Tiffany Ya Tang,et al.  If You Like the Devil Wears Prada the Book, Will You also Enjoy the Devil Wears Prada the Movie? A Study of Cross-Domain Recommendations , 2008, New Generation Computing.

[10]  M.A.S.N. Nunes.  Recommender Systems based on Personality Traits. (Système de Recommendation basé sur Traits de Personnalité) , 2009 .

[11]  P. Ersonality,et al.  Assessing The Impact Of Gender And Personality On Film Preferences , 2010 .

[12]  P. Costa,et al.  NEO inventories for the NEO Personality Inventory-3 (NEO-PI-3), NEO Five-Factor Inventory-3 (NEO-FFI-3), NEO Personality Inventory-Revised (NEO PI-R) : professional manual , 2010 .

[13]  Rong Hu,et al.  A Study on User Perception of Personality-Based Recommender Systems , 2010, UMAP.

[14]  Ran D. Zilca,et al.  Listening, watching, and reading: the structure and correlates of entertainment preferences. , 2011, Journal of personality.

[15]  Marko Tkalcic,et al.  Addressing the New User Problem with a Personality Based User Similarity Measure , 2011 .

[16]  Rong Hu,et al.  Enhancing collaborative filtering systems with personality information , 2011, RecSys '11.

[17]  Yoram Bachrach,et al.  Personality and Website Choice , 2012 .

[18]  Alexandra Roshchina.  TWIN: Personality-based Recommender System , 2012 .

[19]  Iván Cantador,et al.  Cross-domain recommender systems : A survey of the State of the Art , 2012 .

[20]  Rong Hu,et al.  Personality-based recommender systems: an overview , 2012, RecSys.

[21]  Francesco Ricci,et al.  Personality-Based Active Learning for Collaborative Filtering Recommender Systems , 2013, AI*IA.

[22]  Rong Hu,et al.  Exploring Relations between Personality and User Rating Behaviors , 2013, UMAP Workshops.

[23]  Alejandro Bellogín,et al.  Relating Personality Types with User Preferences in Multiple Entertainment Domains , 2013, UMAP Workshops.

[24]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[25]  Jurij F. Tasic,et al.  Personality and Social Context: Impact on Emotion Induction from Movies , 2013, UMAP Workshops.

[26]  Iván Cantador,et al.  Personality-Aware Collaborative Filtering: An Empirical Study in Multiple Domains with Facebook Data , 2014, EC-Web.

[27]  Anita Sharma,et al.  Personality and Patterns of Facebook Usage , 2016 .