Um Benchmark de Algoritmos de Recomendação Baseados em Sessão no Contexto do Mercado Imobiliário Brasileiro
Resumo
O mercado imobiliário representa parcela significativa do PIB brasileiro, mas recomendar imóveis em larga escala impõe desafios próprios: catálogos com alta rotatividade, sessões predominantemente anônimas e interações curtas. Este trabalho propõe um benchmark para recomendação baseada em sessão, utilizando um dataset proprietário do Zap Imóveis com 17,6 milhões de interações. Quinze modelos são comparados sob ranking completo e sob amostragem negativa. Sob catálogo completo, métodos de coocorrência e fatoração superam modelos neurais; sob amostragem negativa, a ordem se inverte parcialmente. A divergência confirma, em um domínio até então não investigado, que o protocolo de avaliação pode alterar a conclusão sobre qual arquitetura é mais adequada.
Palavras-chave:
Recomendação Baseada em Sessão, Sistemas de Recomendação, Benchmark, Mercado Imobiliário, Avaliação de Algoritmos
Referências
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DataZAP (2025). Anuário do mercado imobiliário 2025. Grupo OLX.
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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.
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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.
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Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM), pages 197–206.
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.
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., and Ma, J. (2017). Neural attentive session-based 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.
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.
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.
Ludewig, M. and Jannach, D. (2018). Evaluation of session-based recommendation algorithms. User Model. User-adapt Interact., 28(4-5):331–390.
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.
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.
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.
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.
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.
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.
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.
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 user-friendly and easy-to-use benchmark library for recommender systems. In SIGIR, pages 2837–2847. ACM.
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.
DataZAP (2025). Anuário do mercado imobiliário 2025. Grupo OLX.
Deshpande, M. and Karypis, G. (2004). Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1):143–177.
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.
Gharahighehi, A., Pliakos, K., and Vens, C. (2021). Recommender systems in the real estate market—a survey. Applied Sciences, 11(16).
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.
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.
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.
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.
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.
Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM), pages 197–206.
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.
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., and Ma, J. (2017). Neural attentive session-based 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.
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.
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.
Ludewig, M. and Jannach, D. (2018). Evaluation of session-based recommendation algorithms. User Model. User-adapt Interact., 28(4-5):331–390.
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.
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.
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.
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.
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.
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.
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.
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 user-friendly and easy-to-use benchmark library for recommender systems. In SIGIR, pages 2837–2847. ACM.
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.
Publicado
08/09/2026
Como Citar
TAGARRO, Hygo Freire; MOTA, Vinicius F. S.; COMARELA, Giovanni.
Um Benchmark de Algoritmos de Recomendação Baseados em Sessão no Contexto do Mercado Imobiliário Brasileiro. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 155-168.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249160.
