Comparison of recommendation algorithms for user groups: a food-based case study

Vasconcelos, Caio Giovanni Pereira

Comparison of recommendation algorithms for user groups: a food-based case study

dc.contributor.advisor	Silva, Douglas Véras e
dc.contributor.advisorLattes	http://lattes.cnpq.br/2969243668455081	pt_BR
dc.contributor.author	Vasconcelos, Caio Giovanni Pereira
dc.contributor.authorLattes	http://lattes.cnpq.br/4775036700843482	pt_BR
dc.date.accessioned	2024-07-01T11:32:27Z
dc.date.available	2024-07-01T11:32:27Z
dc.date.issued	2023-04-24
dc.degree.departament	Departamento de Computação	pt_BR
dc.degree.graduation	Bacharelado em Ciência da Computação	pt_BR
dc.degree.grantor	Universidade Federal Rural de Pernambuco	pt_BR
dc.degree.level	Graduacao	pt_BR
dc.degree.local	Recife	pt_BR
dc.description	There is a rise in the development of platforms that work with the distribution of buying and selling food, and with the increase in food options and the number of users, such platforms use recommendation systems to facilitate the user’s choice. These recommendations are usually based on information that the algorithm obtains previously. And increasingly, these recommendations need to be right in specific contexts. This article proposes to compare, through common metrics in the literature, the use of two recommendation algorithms in a context of user groups to make a joint recommendation. One of the algorithms uses a database of groups in neural network training, and the other algorithm uses databases of auxiliary domains with different contexts to perform the prediction. The results indicate that it is possible to perform the prediction for groups of users even if a database with scarce data is used. The article is a theoretical basis to show the efficiency of recommending it to groups in the food domain, and can be incorporated and added to existing platforms.	pt_BR
dc.format.extent	6 f.	pt_BR
dc.identifier.citation	VASCONCELOS, Caio Giovanni Pereira. Comparison of recommendation algorithms for user groups: a food-based case study. 2023. 6 f. Trabalho de Conclusão de Curso (Bacharelado em Ciência da Computação) – Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife, 2023.	pt_BR
dc.identifier.uri	https://repository.ufrpe.br/handle/123456789/5830
dc.language.iso	por	pt_BR
dc.publisher.country	Brasil	pt_BR
dc.rights	openAccess	pt_BR
dc.rights.license	ATRIBUIÇÃO-NÃOCOMERCIAL-SEMDERIVAÇÕES 4.0 INTERNACIONAL (CC BY-NC-ND 4.0)	pt_BR
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/	pt_BR
dc.subject	Sistemas de recomendação (filtragem de informações)	pt_BR
dc.subject	Serviço de alimentação	pt_BR
dc.subject	Algoritmos computacionais	pt_BR
dc.subject	Redes neurais (Computação)	pt_BR
dc.subject	Bancos de dados dedutivos	pt_BR
dc.title	Comparison of recommendation algorithms for user groups: a food-based case study	pt_BR
dc.type	bachelorThesis	pt_BR