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Recommender systems in smart campus: a systematic mapping

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Abstract

Recommender systems are extremely useful tools to provide the user with information that may be of interest. These systems are responsible for performing a series of procedures to filter items from massive databases and return only what the user would be looking for, which can be a product, a song, a movie or series, a website, news, or educational resources. Recommender systems are also intended for educational purposes, returning items such as teaching materials, video classes, books, courses, and short courses, for example. The environments in universities that aggregate these systems are called smart university campus. Sites that make use of multiple technologies, able to relate the virtual environment with the real and provide users with a fully integrated system. From this context, there was a systematic mapping of smart campus areas and recommendation systems. A study was conducted to investigate the relationship between these areas, through the search in four databases, between the years 2017 and 2024, identifying 894 papers, of which 101 were selected for analysis. We also identified some key documents in the area of recommender systems, as well as the technologies applied in each of them. The analysis conducted in this paper identified several research opportunities in the area. However, it was observed that many of the studies do not make clear the information that their applications will be used in conjunction with smart campus.

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Acknowledgements

This work was supported by Fundação de Amparo a Pesquisa do Estado do Rio Grande do Sul (FAPERGS) by Grant No. 21/2551-0000693-5, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) by Grants 306695/2022-7, 405973/2021-7, 306356/2020-1, and 301.425/2018-3.

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  1. Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Polytechnic School, Federal University of Santa Maria (UFSM), Roraima Av. 1000, Santa Maria, Rio Grande do Sul, Brazil

    Martin Hideki Mensch Maruyama, Luan Willig Silveira, Elvandi da Silva Júnior, Gabriel Casanova & Vinícius Maran

  2. Informatics Institute, Federal University of Rio Grande do Sul (UFRGS), Bento Gonçalves Av. 9500, Porto Alegre, Rio Grande do Sul, Brazil

    José Palazzo M. de Oliveira

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Hideki Mensch Maruyama, M., Willig Silveira, L., da Silva Júnior, E. et al. Recommender systems in smart campus: a systematic mapping. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02240-1

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