Comparison of the Non-personalized Active Learning Strategies Used in Recommender Systems

  • Georges ChaayaEmail author
  • Jacques Bou Abdo
  • Elisabeth Métais
  • Raja Chiky
  • Jacques Demerjian
  • Kablan Barbar
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 341)


The study of recommender systems is essential nowadays due to its great effect on businesses and customer satisfaction. Different active learning strategies were previously developed to gain ratings from the users on specific items, and this enables the system to have more information and consequently make more accurate recommendations. In previous studies, these strategies were evaluated using a different selection of metrics in each work, and the experimentations were done on different datasets. In this paper, we solve these weaknesses by comparing the main ten non-personalized strategies on a fair ground, by simulating them against two datasets and using seven of the mostly agreed upon metrics. This gives more trust and less biased results when comparing their performances. Also, the analysis of the computation time and the elicitation efficiency is added.


Recommender systems Collaborative filtering Active learning Cold-start problem Non-personalized strategies Accuracy metrics 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Georges Chaaya
    • 1
    Email author
  • Jacques Bou Abdo
    • 2
  • Elisabeth Métais
    • 1
  • Raja Chiky
    • 3
  • Jacques Demerjian
    • 4
  • Kablan Barbar
    • 4
  1. 1.CEDRIC Laboratory, CNAMParisFrance
  2. 2.Faculty of Natural and Applied SciencesNotre Dame UniversityDeir El KamarLebanon
  3. 3.LISITE Laboratory, ISEPParisFrance
  4. 4.LARIFA-EDST Laboratory, Faculty of SciencesLebanese UniversityFanarLebanon

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