| 1 |
Brasil. Ministério da Fazenda (2025). Relatório de distribuição de renda 2025. Acesso em: 27 fev. 2026.
|
|
| 2 |
DataZAP (2025). Anuário do mercado imobiliário 2025. Grupo OLX.
|
|
| 3 |
Deshpande, M. and Karypis, G. (2004). Item-based top-n recommendation algorithms.
ACM Transactions on Information Systems (TOIS), 22(1):143–177.
|
|
| 4 |
Domingues, M. A., de Moura, E. S., Marinho, L. B., and da Silva, A. (2023). A large
scale benchmark for session-based recommendations in the legal domain. Artificial
Intelligence and Law.
|
|
| 5 |
Gharahighehi, A., Pliakos, K., and Vens, C. (2021). Recommender systems in the real
estate market—a survey. Applied Sciences, 11(16).
|
|
| 6 |
He, R., Kang, W.-C., and McAuley, J. (2017). Translation-based recommendation. In
Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys ’17,
page 161–169, New York, NY, USA. Association for Computing Machinery.
|
|
| 7 |
He, R. and McAuley, J. (2016). Fusing similarity models with markov chains for sparse
sequential recommendation. In 2016 IEEE 16th International Conference on Data
Mining (ICDM), pages 191–200. IEEE.
|
|
| 8 |
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., and Wang, M. (2020). Lightgcn: Simplifying
and powering graph convolution network for recommendation. In Proceedings of the
43rd International ACM SIGIR conference on research and development in Information
Retrieval, pages 639–648.
|
|
| 9 |
Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2016). Session-based recommendations with recurrent neural networks. In Bengio, Y. and LeCun, Y., editors, 4th
International Conference on Learning Representations, ICLR 2016, San Juan, Puerto
Rico, May 2-4, 2016, Conference Track Proceedings.
|
|
| 10 |
Instituto Brasileiro de Geografia e Estatística (IBGE) (2025). Contas nacionais trimestrais:
4º trimestre de 2024. Technical report, IBGE, Rio de Janeiro. Divulgado em 07 de
março de 2025.
|
|
| 11 |
Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In 2018
IEEE International Conference on Data Mining (ICDM), pages 197–206.
|
|
| 12 |
Krichene, W. and Rendle, S. (2020). On sampled metrics for item recommendation. In
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, page 1748–1757, New York, NY, USA. Association for
Computing Machinery.
|
|
| 13 |
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., and Ma, J. (2017). Neural attentive sessionbased recommendation. In Proceedings of the 2017 ACM on Conference on Information
and Knowledge Management, CIKM ’17, page 1419–1428, New York, NY, USA.
Association for Computing Machinery.
|
|
| 14 |
Lima, M., Silva, E., and da Silva, A. (2024). Um estudo sobre o uso de modelos de
linguagem abertos na tarefa de recomendação de próximo item. In Anais do XXXIX
Simpósio Brasileiro de Bancos de Dados, pages 510–522, Porto Alegre, RS, Brasil. SBC.
|
|
| 15 |
Liu, Q., Zeng, Y., Mokhosi, R., and Zhang, H. (2018). Stamp: short-term attention/memory
priority model for session-based recommendation. In Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining, pages
1831–1839.
|
|
| 16 |
Ludewig, M. and Jannach, D. (2018). Evaluation of session-based recommendation
algorithms. User Model. User-adapt Interact., 28(4-5):331–390.
|
|
| 17 |
Ludewig, M., Mauro, N., Latifi, S., and Jannach, D. (2021). Empirical analysis of session based recommendation algorithms. User Model. User-adapt Interact., 31(1):149–181.
|
|
| 18 |
Polohakul, J., Chuangsuwanich, E., Suchato, A., and Punyabukkana, P. (2021). Real
estate recommendation approach for solving the item cold-start problem. IEEE Access,
9:68139–68150.
|
|
| 19 |
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2009). Bpr: Bayesian
personalized ranking from implicit feedback. In Proceedings of the 25th conference on
uncertainty in artificial intelligence, pages 452–461.
|
|
| 20 |
Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2010). Factorizing personalized
markov chains for next-basket recommendation. In Proceedings of the 19th international
conference on World wide web, pages 811–820.
|
|
| 21 |
Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001). Item-based collaborative filtering
recommendation algorithms. In Proceedings of the 10th international conference on
World Wide Web, pages 285–295.
|
|
| 22 |
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P. (2019). Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In
Proceedings of the 28th ACM International Conference on Information and Knowledge
Management, CIKM ’19, page 1441–1450, New York, NY, USA. Association for Computing Machinery.
|
|
| 23 |
Tang, J. and Wang, K. (2018). Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international
conference on web search and data mining, pages 565–573.
|
|
| 24 |
Xu, L., Tian, Z., Zhang, G., Zhang, J., Wang, L., Zheng, B., Li, Y., Tang, J., Zhang, Z.,
Hou, Y., Pan, X., Zhao, W. X., Chen, X., and Wen, J. (2023). Towards a more userfriendly and easy-to-use benchmark library for recommender systems. In SIGIR, pages
2837–2847. ACM.
|
|
| 25 |
Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., and He, X. (2019). A simple convolutional generative network for next item recommendation. In Proceedings of the twelfth
ACM international conference on web search and data mining, pages 582–590.
|
|