Personality-Based Active Learning for Collaborative Filtering Recommender Systems

  • Mehdi Elahi
  • Matthias Braunhofer
  • Francesco Ricci
  • Marko Tkalcic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8249)


Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user’s personality - using the Five Factor Model (FFM) - in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, context-aware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.


Recommender System User Study Random Strategy Rating Request Active Learning Method 
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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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mehdi Elahi
    • 1
  • Matthias Braunhofer
    • 1
  • Francesco Ricci
    • 1
  • Marko Tkalcic
    • 2
  1. 1.Free University of Bozen-BolzanoBozen-BolzanoItaly
  2. 2.Johannes Kepler UniversityLinzAustria

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