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A Deep Autoencoder Based Multi-Criteria Recommender System

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) (AICV 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1377))

Abstract

Traditional recommender systems (RSs) try to quantify the user preferences about items using a numerical value, called rating. However, since employing of RSs has been increased, user expectations have been differentiated. Users may judge items according to different criteria. This gives birth to multicriteria recommender systems where users provide the rating on multiple aspects of an item in new dimensions, thereby increasing the rating matrix’s size and opening up some challenges for researchers in the field, such as sparsity, scalability, and the aggregation of multicriteria rating problems. In this paper, we propose a multicriteria recommender system based on a deep autoencoder to learn the nonlinear relation between users on a multicriteria context in order to reconstruct the missing ratings, and on a Multi-Criteria Decision Making method, which proposes a Correlation Coefficient and Standard Deviation (CCSD) integrated approach to determine the weight of the criteria. We compare our results to some other single and multicriteria recommendation models. The results show that our proposed approach boosts the performance up and outperforms all other methods in terms of recommendation accuracy measures.

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Notes

  1. 1.

    https://grouplens.org/datasets/movielens/.

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Author information

Authors and Affiliations

  1. LM2I Lab, ENSAM, Moulay Ismaïl University, Marjane II, B.P. 15290, Meknès, Morocco

    Yahya Bougteb, Brahim Ouhbi & El Moukhtar Zemmouri

  2. LTTI Lab, Ecole Supérieure de Technologie, B.P. 1796 Atlas, Fès, Morocco

    Bouchra Frikh

Authors
  1. Yahya Bougteb
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  2. Brahim Ouhbi
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  3. Bouchra Frikh
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  4. El Moukhtar Zemmouri
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Corresponding author

Correspondence to Yahya Bougteb .

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. School of Medicine, University of Missouri, Columbia, MO, USA

    Peter J. Tonellato

  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex, France

    Ladjel Bellatreche

  5. British University Vietnam, Hung Yen, Vietnam

    Sam Goundar

  6. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmad Taher Azar

  7. NEST Research Group, ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  8. High National School for Computer Science and Systems Analysis (ENSIAS), Mohammed V University, Rabat, Morocco

    Driss Bouzidi

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Bougteb, Y., Ouhbi, B., Frikh, B., Zemmouri, E.M. (2021). A Deep Autoencoder Based Multi-Criteria Recommender System. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_6

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