Applying a post-processing strategy to consider the multiple interests of users of a Paper Recommender System

  • Nathália Locatelli Cezar Universidade do Estado de Santa Catarina
  • Caroline de Borba Universidade do Estado de Santa Catarina
  • Isabela Gasparini Universidade do Estado de Santa Catarina
  • Daniel Lichtnow Universidade Federal de Santa Maria


Currently, the amount of information available to Web users is very large, and this situation is similar for scientific communities when searching for papers for their research. Recommender Systems (RSs) can help in this task because they combine computational techniques to select personalized items based on the users’ interests and according to the context in which users are inserted. The increase in the impact and scope of recommendations in the users’ lives, leads to the result on the ethical issues involved in the generation of recommendations and indicators for visualizing the results of the algorithms found. This paper presents a Recommender System for the Human-Computer Interaction (HCI) community, indicating papers from the Brazilian Symposium on Human Factors in Computing Systems related to the users’ profile applied to a post-processing strategy focused on fairness to balance the users’ interests. After the development of the RS and the Web environment, the results were obtained on the impact that the tool had on the community and demonstrated through the evaluation of the system.
Palavras-chave: Recommender Systems, HCI, fairness


Gediminas Adomavicius and Alexander Tuzhilin. [n.d.]. Toward the Next Generation of Recommender Systems : A Survey of the State-of-the-Art and Possible Extensions. IEEE Access 17, 6 ([n. d.]), 734–749. 

Xiaomei Bai, Mengyang Wang, Ivan Lee, Zhuo Yang, Xiangjie Kong, and Feng Xia. 2019. Scientific Paper Recommendation : A Survey. IEEE Access 7(2019), 9324–9339.

Marko Balabanović and Yoav Shoham. 1997. Content-Based, Collaborative Recommendation. Commun. ACM 40, 3 (1997), 66–72.

Solon Barocas and Moritz Hardt. 2014. Fairness, accountability, and transparency in machine learning. (2014).

Solon Barocas and Andrew D. Selbst. 2018. Big Data’s Disparate Impact. SSRN Electronic Journal 671 (2018), 671–732.

Eduardo Borba. 2015. Sistema de Recomendação de Objetos de Aprendizagem no Ambiente Adaptweb. Trabalho de Conclusão de Curso - Bacharelado em Ciência da Computação (2015).

Nathália Locatelli Cezar, Isabela Gasparini, and Daniel Lichtnow. 2019. Sistema de recomendação para sugerir artigos para a Comunidade Brasileira de IHC. Monografia (Bacharel em Ciência da Computação), UDESC (Universidade do Estado de Santa Catarina), Joinville, Brasil.

Gilberto Consoni. 2014. Recuperação de informação em sistemas de recomendação: análise da interação mediada por computador e dos efeitos da filtragem colaborativa na seleção de itens no website da Universidade Federal do Rio Grande do Sul (UFRGS).

Michael D. Ekstrand, Robin Burke, and Fernando Diaz. 2019. Fairness and discrimination in retrieval and recommendation: Half-day tutorial. SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019), 1403–1404.

Elena Gaudioso and Féliz Hernández Del Olmo. 2008. Evaluation of recommender systems : A new approach. Expert Systems with Applications: An International Journal 35 (2008), 790–804.

Clinton Gormley and Zachary Tong. 2015. Elasticsearch: The Definitive Guide(1 ed.). O’Reilly Media, Inc. 

Daniel Lichtnow. [n.d.]. Sistemas de Recomendação: Breve Histórico e Perspectivas. ([n. d.]), 88–105.

Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2009. An Introduction to Information RetrievalAn Introduction to Information Retrieval. Number c. Cambridge University Press.

Yuri Nakao, Junichi Shigezumi, Hikaru Yokono, and Takuya Takagi. 2019. Requirements for Explainable Smart Systems in the Enterprises from Users and Society Based on FAT. In IUI Workshops’19.

Jessé Peixoto. 2019. Como o elasticsearch decide o que é relevante e ordena o resultado de uma busca? [link].

Pearl Pu, Li Chen, and Rong Hu. 2012. Evaluating recommender systems from the user’s perspective: survey of the state of the art. In User Modeling and User-Adapted Interaction, Vol. 22. 317-355.

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2010. Introduction to Recommender Systems Handbook. In Recommender Systems Handbook, Vol. 22. 1–35.

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Recommender Systems: Handbook(2011 ed.). Springer. 

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems Handbook. Springer US. 

Andre Roque. 2019. A Tutela Coletiva dos Dados Pessoais na Lei Geral de Proteção de Dados Pessoais (LGPD), Vol. 20. Revista Eletrônica de Direito Processual.

Andrew D. Selbst and Julia Powles. 2017. Meaningful Information and the Right to Explanation. In International Data Privacy Law, Vol. 7. Oxford University Press, 233–242.

K. Sparck Jones, S. Walker, and S. E. Robertson. 2000. Probabilistic model of information retrieval: Development and comparative experiments. Part 2. Information Processing and Management 36, 6 (2000), 809–840.

Harald Steck. 2018. Calibrated Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 154–162.

Colin Tankard. 2016. What the GDPR means for businesses, Vol. 2016. Elsevier, 5–8.

Paul Voigt and Axel von dem Bussche. 2017. The EU General Data Protection Regulation (GDPR): A Practical Guide. Springer International Publishing.
Como Citar

Selecione um Formato
CEZAR, Nathália Locatelli; DE BORBA, Caroline; GASPARINI, Isabela; LICHTNOW, Daniel. Applying a post-processing strategy to consider the multiple interests of users of a Paper Recommender System. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, Uberlândia. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 .

Artigos mais lidos do(s) mesmo(s) autor(es)