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English Information

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Authors
# Name
1 Kirk Sahdo(kmis.eng21@uea.edu.br)
2 Tiago de Melo(tmelo@uea.edu.br)

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Reference
# Reference
1 Abonizio, H., Almeida, T. S., Laitz, T., Malaquias Junior, R., Bonás, G. K., Nogueira, R., and Pires, R. (2024). Sabiá-3 technical report. Technical report, Maritaca AI.
2 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901.
3 de Melo, T., da Silva, A. S., de Moura, E. S., and Calado, P. (2019). Opinionlink: Leveraging user opinions for product catalog enrichment. Information Processing & Management, 56(3):823–843.
4 Dong, X. L. (2018). Challenges and innovations in building a product knowledge graph. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 2869.
5 Du, Y., Zhu, X., Chen, L., Fang, Z., and Gao, Y. (2023). Metakg: Meta-learning on knowledge graph for cold-start recommendation. IEEE Transactions on Knowledge and Data Engineering, 35(10).
6 Guo, X., Yin, S.-C., Zhang, Y., Li, W., and He, Q. (2019). Cold start recommendation based on attribute-fused singular value decomposition. IEEE Access, 7:11349–11359.
7 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, 54(4):1–37.
8 Kotsifas, M. F. A., Lüders, R., and Silva, T. H. (2025). How culture shapes customers: A cross-continent analysis of apps reviews using NLP techniques. In Anais do XL Simpósio Brasileiro de Banco de Dados (SBBD), pages 760–766.
9 Li, X., Zhang, K., and Han, D. (2025). Duality-driven aspect sentiment triplet extraction with llm and iterative reinforcement. Symmetry, 17(5):642.
10 Moreira, J., de Melo, T., Barbosa, L., and da Silva, A. (2022). A distantly supervised approach for enriching product graphs with user opinions. Journal of Intelligent Information Systems, 59:435–454.
11 Perrone, G., Unpingco, J., and Lu, H.-m. (2020). Network visualizations with pyvis and visjs. arXiv preprint arXiv:2006.04951.
12 Simmering, P. F. and Huoviala, P. (2023). Large language models for aspect-based sentiment analysis. arXiv preprint arXiv:2310.18025.
13 Wang, M., Guo, Y., Zhang, D., Jin, J., Li, M., Schonfeld, D., and Zhou, S. (2024). Enabling explainable recommendation in e-commerce with llm-powered product knowledge graph. arXiv preprint arXiv:2412.01837.
14 Wang, X., He, X., Cao, Y., Liu, M., and Chua, T.-S. (2019). Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 950–958.
15 Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K. (2020). Product knowledge graph embedding for e-commerce. In Proceedings of the 13th International Conference on Web Search and Data Mining, pages 672–680.
16 Yuan, H. and Hernandez, A. A. (2023). User cold start problem in recommendation systems: A systematic review. IEEE Access, 11:136958–136977.