Implicit acquisition of user personality for augmenting movie recommendations

Wen Wu*, Li CHEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

29 Citations (Scopus)


In recent years, user personality has been recognized as valuable info to build more personalized recommender systems. However, the effort of explicitly acquiring users’ personality traits via psychological questionnaire is unavoidably high, which may impede the application of personality-based recommenders in real life. In this paper, we focus on deriving users’ personality from their implicit behavior in movie domain and hence enabling the generation of recommendations without involving users’ efforts. Concretely, we identify a set of behavioral features through experimental validation, and develop inference model based on Gaussian Process to unify these features for determining users’ big-five personality traits. We then test the model in a collaborative filtering based recommending framework on two real-life movie datasets, which demonstrates that our implicit personality based recommending algorithm significantly outperforms related methods in terms of both rating prediction and ranking accuracy. The experimental results point out an effective solution to boost the applicability of personality-based recommender systems in online environment.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation and Personalization - 23rd International Conference, UMAP 2015, Proceedings
EditorsKalina Bontcheva, Francesco Ricci, Owen Conlan, Séamus Lawless
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)9783319202662
Publication statusPublished - 2015
Event23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015 - Dublin, Ireland
Duration: 29 Jun 20153 Jul 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Collaborative filtering
  • Implicit acquisition
  • Recommender systems
  • User personality


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