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The Use of Multiple Criteria Decision Aiding Methods in Recommender Systems: A Literature Review

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Intelligent Systems (BRACIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13653))

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Abstract

Multiple Criteria Decision Making (MCDA) methods have been increasingly applied to improve recommendations when multiple criteria are considered in Recommender Systems (RSs). This study presents the preliminary results of a systematic literature review, following Kitchenham’s guidelines, regarding the application of MCDA methods in RSs over the last two decades. Based on our findings, MCDA methods can be applied in two RS phases: the preference elicitation and the recommendation phases. In the former, RSs usually have a strong interaction with the user, which results in more personalized recommendations, ensuring higher user satisfaction and contributing to address the cold-start challenge in RSs. Regarding the recommendation phase, while most RSs are based on ranking approaches, there is a trend to apply sorting methods in order to avoid an additional step involving a filtering application that selects a subset of alternatives. Future research could focus on applying preference learning combined with MCDA methods for exploring improvements in prediction and recommendation phases, and also in quality and processing time.

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Acknowledgment

This work was supported by the São Paulo Research Foundation (FAPESP), grants #2018/23447 and #2020/01089-9, and the Brazilian National Council for Scientific and Technological Development (CNPq). This project is also part of the Brazilian Institute of Data Science, grant #2020/09838-0, São Paulo Research Foundation (FAPESP).

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Authors and Affiliations

  1. School of Applied Sciences, University of Campinas, Limeira, Brazil

    Renata Pelissari & Leonardo Tomazeli Duarte

  2. Computer Science Department, University of Waterloo, Waterloo, Canada

    Paulo S. Alencar

  3. Telfer School of Management, University of Ottawa, Ottawa, Canada

    Sarah Ben Amor

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  1. Renata Pelissari
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  2. Paulo S. Alencar
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  4. Leonardo Tomazeli Duarte
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Correspondence to Renata Pelissari .

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  1. Federal University of Rio Grande do Norte, Natal, Brazil

    João Carlos Xavier-Junior

  2. Federal University of Bahia, Salvador, Brazil

    Ricardo Araújo Rios

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Pelissari, R., Alencar, P.S., Amor, S.B., Duarte, L.T. (2022). The Use of Multiple Criteria Decision Aiding Methods in Recommender Systems: A Literature Review. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13653. Springer, Cham. https://doi.org/10.1007/978-3-031-21686-2_37

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