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REFERENCES
Adomavicius, G. and Tuzhilin, A. (2005). Toward the
next generation of recommender systems: A sur-
vey of the state-of-the-art and possible extensions.
IEEE Transactions on Knowledge & Data Engineer-
ing, 17(6):734–749.
Brunialti, L. F., Freire, V., Peres, S., and Lima, C. A.
d. M. (2015). aprendizado de m
´
aquina em sistemas
de recomendac¸
˜
ao baseados em conteudo textual uma
revisao sistematica. In XI Brazilian Symposium on In-
formation System, pages 203–210, Goi
ˆ
ania. Associa-
tion of Information Systems.
Buchinger, D., de Siqueira Cavalcanti, G. A., and
da Silva Hounsell, M. (2014). Mecanismos de
busca acad
ˆ
emica: uma an
´
alise quantitativa. Revista
Brasileira de Computac¸
˜
ao Aplicada, 6(1):108–120.
Burke, R. (2002). Hybrid recommender systems: Survey
and experiments. User modeling and user-adapted in-
teraction, 12(4):331–370.
Burke, R. (2007). Hybrid web recommender systems. In
The adaptive web, pages 377–408. Springer, Switzer-
land.
Champiri, Z. D., Shahamiri, S. R., and Salim, S. S. B.
(2015). A systematic review of scholar context-aware
recommender systems. Expert Systems with Applica-
tions, 42(3):1743–1758.
Diniz, E. (2013). Editorial. Revista de Administrac¸
˜
ao de
Empresas, 53:223 – 223.
Ibrahima, O. A. S. and Younisb, E. M. (2018). Recom-
mender systems and their fairness for user prefer-
ences: A literature study.
Jannach, D., Zanker, M., Felfernig, A., and Friedrich,
G. (2010). Recommender systems: an introduction.
Cambridge University Press, UK.
Kitchenham, B. (2004). Procedures for performing sys-
tematic reviews. Keele, UK, Keele University,
33(2004):1–26.
Malhotra, R. (2015). A systematic review of machine learn-
ing techniques for software fault prediction. Applied
Soft Computing, 27:504–518.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., and Ngo,
D. C. L. (2014). Text mining for market prediction: A
systematic review. Expert Systems with Applications,
41(16):7653–7670.
Palaniappan, R., Sundaraj, K., and Ahamed, N. U. (2013).
Machine learning in lung sound analysis: a systematic
review. Biocybernetics and Biomedical Engineering,
33(3):129–135.
Park, D. H., Kim, H. K., Choi, I. Y., and Kim, J. K. (2012).
A literature review and classification of recommender
systems research. Expert Systems with Applications,
39(11):10059–10072.
Petersen, K., Vakkalanka, S., and Kuzniarz, L. (2015).
Guidelines for conducting systematic mapping stud-
ies in software engineering: An update. Information
and Software Technology, 64:1–18.
Pons, E., Braun, L. M., Hunink, M. M., and Kors, J. A.
(2016). Natural language processing in radiology: a
systematic review. Radiology, 279(2):329–343.
Portugal, I., Alencar, P., and Cowan, D. (2017). The use
of machine learning algorithms in recommender sys-
tems: a systematic review. Expert Systems with Appli-
cations.
Ricci, F., Shapira, B., and Rokach, L. (2015). Recommender
systems handbook, Second edition. Springer, Switzer-
land.
Skiena, S. S. (2017). The Data Science Design Manual.
Springer, Switzerland.
Taghavi, M., Bentahar, J., Bakhtiyari, K., and Hanachi,
C. (2017). New insights towards developing recom-
mender systems. The Computer Journal, 61(3):319–
348.
Tan, P.-N., Steinbach, M., and Kumar, V. (2009).
Introduc¸
˜
ao ao Data Mining Minerac¸
˜
ao de Dados.
Ci
ˆ
encia Moderna, Rio de Janeiro.
Wazlawick, R. (2017). Metodologia de pesquisa para
ci
ˆ
encia da computac¸
˜
ao, volume 2. Elsevier Brasil,
Rio de Janeiro.
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