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

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Information Systems (EMCIS 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 341))

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Abstract

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.

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Author information

Authors and Affiliations

  1. CEDRIC Laboratory, CNAM, Paris, France

    Georges Chaaya & Elisabeth Métais

  2. Faculty of Natural and Applied Sciences, Notre Dame University, Deir El Kamar, Lebanon

    Jacques Bou Abdo

  3. LISITE Laboratory, ISEP, Paris, France

    Raja Chiky

  4. LARIFA-EDST Laboratory, Faculty of Sciences, Lebanese University, Fanar, Lebanon

    Jacques Demerjian & Kablan Barbar

Authors
  1. Georges Chaaya
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  2. Jacques Bou Abdo
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  3. Elisabeth Métais
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  4. Raja Chiky
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  5. Jacques Demerjian
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  6. Kablan Barbar
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Corresponding author

Correspondence to Georges Chaaya .

Editor information

Editors and Affiliations

  1. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. University of Coimbra, Coimbra, Portugal

    Paulo Rupino da Cunha

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Chaaya, G., Abdo, J.B., Métais, E., Chiky, R., Demerjian, J., Barbar, K. (2019). Comparison of the Non-personalized Active Learning Strategies Used in Recommender Systems. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_34

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  • DOI: https://doi.org/10.1007/978-3-030-11395-7_34

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