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English Information

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Authors
# Name
1 Antony Seabra(amede@bndes.gov.br)
2 Claudio Cavalcante(cfrag@bndes.gov.br)
3 Sergio Lifschitz(sergio@inf.puc-rio.br)

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Reference
# Reference
1 Aggarwal, C. C. et al. (2016). Recommender systems, volume 1. Springer.
2 Balloccu, G., Boratto, L., Fenu, G., Malloci, F. M., and Marras, M. (2024). Explainable recommender systems with knowledge graphs and language models. In European Conference on Information Retrieval, pages 352–357. Springer.
3 Giray, L. (2023). Prompt engineering with chatgpt: a guide for academic writers. Annals of biomedical engineering, 51(12):2629–2633.
4 Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Computing Surveys (Csur), 54(4):1–37.
5 Ji, S., Pan, S., Cambria, E., Marttinen, P., and Philip, S. Y. (2021). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE transactions on neural networks and learning systems, 33(2):494–514.
6 Lops, P., De Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, pages 73–105.
7 Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E. (2015). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1):11–33.
8 Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X. (2024). Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering.
9 Pascanu, R., Mikolov, T., and Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In International conference on machine learning, pages 1310–1318. Pmlr.
10 Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web: methods and strategies of web personalization, pages 291–324. Springer.
11 Seabra, A., Cavalcante, C., Nepomuceno, J., Lago, L., Ruberg, N., and Lifschitz, S. (2024). Contrato360: uma aplicacção de perguntas e respostas usando modelos de linguagem, documentos e bancos de dados. In Simp´osio Brasileiro de Banco de Dados (SBBD), pages 155–166. SBC.
12 Shen, W., Wang, J., and Han, J. (2014). Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2):443–460.
13 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
14 Wang, M.,Wang, M., Xu, X., Yang, L., Cai, D., and Yin, M. (2023). Unleashing chatgpt’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering. IEEE Transactions on Learning Technologies.
15 Wang, X., He, X.,Wang, M., Feng, F., and Chua, T.-S. (2019). Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pages 165–174.
16 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint rXiv:2302.11382.
17 Zhang, S., Yao, L., Sun, A., and Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1):1–38.