A content-based recommendation system for animal adoption using the weighted cosine technique
DOI:
https://doi.org/10.1590/1983-3652.2024.42708Keywords:
Recommender system, Content-based recommendation, Adoption of animals, Dog adoption, Weighted cosine techniqueAbstract
An adopted animal is considered a grand gesture with significant social responsibility since it needs daily care and affection from the adopter. Far beyond appearance and aesthetic issues, the adopter must consider their natural conditions, respecting the specific characteristics of the animal. The lack of expertise in choosing the animal can lead to an irresponsible and fruitless adoption, compromising the animal's quality of life. Animal adoption portals are prone to this problem if they do not provide controls that try to approximate the adopter's real motivations and conditions with the animal's characteristics. Faced with this problem, this article proposes a content-based recommendation system to suggest animals for users to consume drugs, applying the weighted cosine technique. The weighting is fundamental in calculating the similarity between the users' emotions and the animal's characteristics. The experiments increased the accuracy of the recommendations when weighting is applied over the traditional method without considering the prescribed method of users by specific characteristics of the animals to be adopted.
Downloads
References
AGGARWAL, Charu C. An Introduction to Recommender Systems. In: RECOMMENDER Systems: The Textbook. Cham: Springer International Publishing, 2016. P. 1–28. ISBN 978-3-319-29659-3. DOI: 10.1007/978-3-319-29659-3_1.
ALEJO, García-Rodríguez et al. Avances en la gestión de un centro de acogida de animales de compañía. Scielo, p. 76–78, 2008. DOI: 10.1157/13115116.
ALVES, Ana et al. Abandono de cães na América Latina: revisão de literatura. Revista de Educação Continuada em Medicina Veterinária e Zootecnia do CRMV-SP, v. 11, p. 34–41, 2013. DOI: https://doi.org/10.36440/recmvz.v11i2.16221.
ASPCA. Meet Your Match Pet Adoption Program. Disponível em: https://www.aspcapro.org/resource/meet-your-match-pet-adoption-program.
AL-BASHIRI, Hael et al. Collaborative Filtering Similarity Measures: Revisiting. In: PROCEEDINGS of the International Conference on Advances in Image Processing. Bangkok, Thailand: Association for Computing Machinery, 2017. (ICAIP 2017), p. 195–200. ISBN 9781450352956. DOI: 10.1145/3133264.3133299. Disponível em: https://doi.org/10.1145/3133264.3133299.
BURKE, Robin. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, Kluwer Academic Publishers, Hingham, MA, USA, v. 12, n. 4, p. 331–370, nov. 2002. ISSN 0924-1868.
CHENG, Wai Khuen et al. Affective Recommender System for Pet Social Network. Sensors, v. 22, n. 18, 2022. ISSN 1424-8220. DOI: 10.3390/s22186759. Disponível em: https://www.mdpi.com/1424-8220/22/18/6759.
CHYAN, Phie. Design of Purebred Dog Recommendation System Using MCDM Approach. Advances in Science, Technology and Engineering Systems Journal, v. 5, n. 5, p. 148–153, 2020. DOI: 10.25046/aj050520.
DIAS DE ALMEIDA, Joao Paulo; DURAO, Frederico Araujo. Personalizing the Top-k Spatial Keyword Preference Query with textual classifiers. Expert Systems with Applications, v. 162, p. 113841, 2020. ISSN 0957-4174. DOI: https://doi.org/10.1016/j.eswa.2020.113841. Disponível em: https://www.sciencedirect.com/science/article/pii/S0957417420306503.
ELEY, Amy. Lost and found: App uses facial recognition technology to find missing pets. 2014. Disponível em: https://www.today.com/style/lost-found-app-uses-facial-recognition-technology-find-missing-pets-2d79501254. Acesso em: 13 abr. 2023.
FALCONERY, Lucas. Cães e gatos adotados em Fortaleza recebem microchips para evitar abandono. [S.l.]: G1-CE, 2019. Disponível em: https://g1.globo.com/ce/ceara/noticia/2019/07/29/caes-e-gatos-adotados-em-fortaleza-recebem-microchips-/para-evitar-abandono.ghtml.
FAPESP. Aplicativo utiliza visão computacional para identificar animais perdidos. [S.l.]: FAPESP, 2017. Disponível em: http://pesquisaparainovacao.fapesp.br/aplicativo_utiliza_visao_computacional_para_identificar_animais_perdidos/463.
FURCODE. Disponível em: https://www.pethub.com/furcode.
GARUN, Natt. A Chinese AI startup is tracking lost dogs using their nose prints. 2019. Disponível em: https://www.theverge.com/2019/7/13/20693064/megvii-chinese-ai-facial-recognition-lost-pets-dogs-cats-surveillance. Acesso em: 13 abr. 2023.
GOLDBERG, David et al. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM, ACM, New York, NY, USA, v. 35, n. 12, p. 61–70, dez. 1992. ISSN 0001-0782. DOI: 10.1145/138859.138867. Disponível em: http://doi.acm.org/10.1145/138859.138867.
GOMES, Caroline Cavalcante Maia. Guarda responsável de animais de companhia: um estudo sobre a responsabilidade civil dos proprietários e a entrega de cães e gatos na Diretoria de Vigilância Ambiental do Distrito Federal. [S.l.: s.n.], 2013. Último acesso em 22 de maio de 2022. Disponível em: http://bdm.unb.br/handle/10483/5985.
HERLOCKER, Jonathan L. et al. Evaluating Collaborative Filtering Recommender Systems. ACM Trans. Inf. Syst., ACM, New York, NY, USA, v. 22, n. 1, p. 5–53, jan. 2004. ISSN 1046-8188. DOI: 10.1145/963770.963772. Disponível em: http://doi.acm.org/10.1145/963770.963772.
IAMS. Disponível em: https://www.iams.com/dog-breed-selector.
IBOPE. Au.Dote. [S.l.: s.n.], 2015. Disponível em: http://www.ibopeinteligencia.com/noticias-e- pesquisas/pesquisa-traca-o-perfil-dos-proprietarios-de-pets-no-bra/sil/. [Último acesso em 22 de maio de 2022]. Disponível em: %7Bhttp://www.ibopeinteligencia.com/noticias-e-pesquisas/pesquisa-traca-o-perfil-dos-proprietarios-de-pets-no-bra/sil/%7D.
JEYARAJ, Pradeepa; APONSO, Achala. A Review of Techniques for Image Classification to Enhance Online Animal Adoption Speed. In: PROCEEDINGS of the 2020 12th International Conference on Computer and Automation Engineering. Sydney, NSW, Australia: Association for Computing Machinery, 2020. (ICCAE 2020), p. 114–118. ISBN 9781450376785. DOI: 10.1145/3384613.3384656. Disponível em: https://doi.org/10.1145/3384613.3384656.
KAJBAJE, Sumit; SAWANT, Rohit; PATIL, Ronit Loke3 Vishwajeet. AI-Based Pet Adoption System. International Research Journal of Engineering e Technology (IRJET), 2022. ISSN 2395-0056. Disponível em: https://www.irjet.net/archives/V9/i4/IRJET-V9I4298.pdf.
KARAM, Orlando; HALSTEAD-NUSSLOCH, Richard. Introducton to Android Development. J. Comput. Sci. Coll., Consortium for Computing Sciences in Colleges, Evansville, IN, USA, v. 28, n. 2, p. 224, out. 2012. ISSN 1937-4771.
KONSTAN, Joseph A. Introduction to Recommender Systems. In: PROCEEDINGS of the 2008 ACM SIGMOD International Conference on Management of Data. Vancouver, Canada: Association for Computing Machinery, 2008. (SIGMOD ’08), p. 1373–1374. ISBN 9781605581026. DOI: 10.1145/1376616.1376776. Disponível em: https://doi.org/10.1145/1376616.1376776.
LOPS, P.; GEMMIS, M. de; SEMERARO, G. Content-based recommender systems: State of the art and trends. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook. In: [s.l.]: Springer US, 2011. P. 73–105. ISBN 978-0-387-85820-3. DOI: 10.1007/978-0-387-85820-3_3.
MAHARRY, Dan. Getting Up to Speed with TypeScript. In: [s.l.: s.n.], jan. 2013. P. 1–26. ISBN 978-1-4302-5725-7. DOI: 10.1007/978-1-4302-5726-4_1.
MATOS, Liziane Gonçalves d. Quando a ajuda é animalitária : um estudo antropológico sobre sensibilidades e moralidades envolvidas no cuidado e proteção de animais abandonados a partir de Porto Alegre-RS. [S.l.: s.n.], 2012. Último acesso em 22 de maio de 2022. Disponível em: http://hdl.handle.net/10183/56016.
METRÓPOLES. Sem raça definida. [S.l.]: Metrópoles - Portal do Dog, 2020. Último acesso em 22 de maio de 2022. Disponível em: https://www.portaldodog.com.br/cachorros/racas-cachorros/sem-raca-definida-srd/.
MLADENIC, D. Text-learning and related intelligent agents: A survey. [S.l.]: IEEE Intelligent Systems, 1999. Último acesso em 22 de maio de 2022. Disponível em: https://ieeexplore.ieee.org/document/784084.
NAHRA, Alessandra. Tecnologia e adoção de animais. [S.l.: s.n.], 2015. Disponível em: http://www.saiba-mais.com/2015/07/23/tecnologia-e-adocao-de-animais/.
ORLANDI, Claudio. Firebase: serviços, vantagens, quando utilizar e integrações. [S.l.: s.n.], 2019. Último acesso em 22 de maio de 2022. Disponível em: https://blog.rocketseat.com.br/firebase/.
PEDERNEIRAS, Gabriela. App chinês usa reconhecimento facial para identificar animais perdidos. [S.l.]: Tecmundo, 2022. Disponível em: https://www.tecmundo.com.br/software/143909-app-chines-usa-reconhecimento-facial-identificar/-animais-perdidos.htm.
PRODUITS NESTLÉ S.A., Société des. Dogs for adotion. Disponível em: https://www.petfinder.com/search/dogs-for-adoption/?sort%5B0%5D=recently_added. Acesso em: 13 abr. 2023.
PROTOPOPOVA, A; GUNTER, LM. Adoption and relinquishment interventions at the animal shelter: a review. [S.l.]: Cambridge University Press, 2017. v. 26, p. 35–48. DOI: 10.7120/09627286.26.1.035.
RESNICK, Paul; VARIAN, Hal R. Recommender Systems. Commun. ACM, ACM, New York, NY, USA, v. 40, n. 3, p. 56–58, mar. 1997. ISSN 0001-0782. DOI: 10.1145/245108.245121. Disponível em: http://doi.acm.org/10.1145/245108.245121.
RICCI, Francesco; ROKACH, Lior; SHAPIRA, Bracha. Introduction to Recommender Systems Handbook. In: [s.l.]: Springer US, 2011. P. 1–35. ISBN 978-0-387-85820-3. DOI: 10.1007/978-0-387-85820-3_1.
SALEEM, Asma et al. Pre-Processing Methods of Data Mining. In: PROCEEDINGS of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing. USA: IEEE Computer Society, 2014. (UCC ’14), p. 451–456. ISBN 9781479978816. DOI: 10.1109/UCC.2014.57. Disponível em: https://doi.org/10.1109/UCC.2014.57.
SILVA, Diego Corrêa da. Ouvido musical: explorando metadados em um sistema de recomendação de músicas. [S.l.]: RI UFBA, 2019. Último acesso em 22 de maio de 2022. DOI: 10.13140/RG.2.2.26829.56800. Disponível em: http://repositorio.ufba.br/ri/handle/ri/29739.
SIQUEIRA SANTOS, Matheus Gallo de et al. PET WORLD: Algoritmos de recomendações aplicados no mundo animal. RE3C-Revista Eletrônica Científica de Ciência da Computação, v. 16, n. 1, 2021.
SONG, Hee Seok; KIM, Young Ae. A dog food recommendation system based on nutrient suitability. Expert Systems, v. 38, n. 2, e12623, 2021. DOI: https://doi.org/10.1111/exsy.12623.
SOUZA, Francisco et al. FugaPet-Rotas: Um algoritmo inteligente para recomendação de rotas visando buscar animais desaparecidos. In: ANAIS da VI Escola Regional de Engenharia de Software. Blumenau: SBC, 2022. P. 199–208. DOI: 10.5753/eres.2022.227993. Disponível em: https://sol.sbc.org.br/index.php/eres/article/view/22387.
STAFFORD, Kevin. The Welfare of Dogs. In: [s.l.: s.n.], 2007. ISBN 9781402043611.
TOZZI, Tatiana; ANDERLE, Daniel Fernando; NOGUEIRA, Rodrigo Ramos. Levantamento de Tecnologias para ONGs de Proteção Animal para apoio ao resgate de animais domésticos acoplados ao ciclo de vida de um sistema Web. In: ANAIS Estendidos do XXIV Simpósio Brasileiro de Sistemas Multimídia e Web. [S.l.]: SBC, 2018. P. 81–84. DOI: 10.5753/webmedia.2018.4574. Disponível em: https://sol.sbc.org.br/index.php/webmedia_estendido/article/view/4063.
TOZZI, Tatiana; ANDERLE, Daniel Fernando; NOGUEIRA, Rodrigo Ramos. Levantamento de tecnologias para ONGs de Proteção Animal para apoio ao resgate de animais domésticos acoplados ao ciclo de vida de um Sistema Web. In: SBC. ANAIS Estendidos do XXIV Simpósio Brasileiro de Sistemas Multimídia e Web. [S.l.: s.n.], 2018. P. 81–84.
WENG, Hsin-Yi et al. Risk factors for unsuccessful dogownership: An epidemiologic study in Taiwan. Elsevier, p. 82–85, 2006. DOI: 10.1016/j.prevetmed.2006.06.004.
ZANGERLE, Eva; BAUER, Christine. Evaluating Recommender Systems: Survey and Framework. ACM Comput. Surv., Association for Computing Machinery, New York, NY, USA, v. 55, n. 8, out. 2022. ISSN 0360-0300. DOI: 10.1145/3556536. Disponível em: https://doi.org/10.1145/3556536.
ZHANG, Shuai et al. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv., Association for Computing Machinery, New York, NY, USA, v. 52, n. 1, fev. 2019. ISSN 0360-0300. DOI: 10.1145/3285029. Disponível em: https://doi.org/10.1145/3285029.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Monaliza Carvalho Santos, Lorena Lima da Silveira dos Santos, Vítor Hugo Barbosa dos Santos, Guilherme Souza Brandão, Frederico Araújo Durão
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open access article that allows unrestricted use, distribution and reproduction in any medium as long as the original article is properly cited.