Quando Comentários e Notas Discordam: Implicações para Sistemas de Recomendação Baseados em Reviews
Resumo
Este trabalho investiga a relação entre notas e comentários no contexto de recomendação, avaliando inconsistências entre essas duas formas de interação, o potencial de notas inferidas a partir do texto e o impacto efetivo dos comentários em modelos Review-Aware Recommender Systems (RARs). Para isso, avaliamos três modelos estado-da-arte em três coleções amplamente utilizadas e empregamos a versão zero-shot do BERT para converter comentários em valores numéricos na mesma escala das notas originais. Os resultados mostram que há discrepâncias relevantes entre nota e comentário, que notas inferidas a partir do texto podem recuperar mais itens relevantes e que os RARs avaliados permanecem fortemente dependentes da nota original.
Palavras-chave:
Sistemas de Recomendação, Sistemas de Recomendação Baseados em Avaliações Textuais
Referências
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Bittencourt, G., Vasconcelos, N., Andrade, Y., Silva, N., Cunha, W., Colombo Dias, D. R., Gonçalves, M. A., and Rocha, L. (2026). Review-aware recommender systems (rarss): Recent advances, experimental comparative analysis, discussions, and new directions. ACM Comput. Surv., 58(1).
Dang, C., Moreno García, M., and De La Prieta, F. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21:5666.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., and Solorio, T., editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186.
Ganu, G., Elhadad, N., and Marian, A. (2009). Beyond the stars: improving rating predictions using review text content. In 12th International Workshop on the Web and Databases (WebDB), pages 1–6.
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Li, D., Liu, H., Zhang, Z., Lin, K., Fang, S., Li, Z., and Xiong, N. N. (2021). Carm: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms. Neurocomputing, 455:283–296.
Liu, H., Wang, Y., Peng, Q., Wu, F., Gan, L., Pan, L., and Jiao, P. (2020). Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing, 374:77–85.
Liu, J., Li, T., Yu, M., Yang, S., Tang, Z., and Yang, Z. (2025). A multi-factor collaborative prediction for review-based recommendation. In Proceedings of the Nineteenth ACM RecSys.
Ni, J., Li, J., and McAuley, J. (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Inui, K., Jiang, J., Ng, V., and Wan, X., editors, Proceedings o EMNLP-IJCNLP.
Ricci, F., Rokach, L., and Shapira, B. (2015). Recommender systems: introduction and challenges. Recommender systems handbook, pages 1–34.
Sayeed, M. S., Roji, V., and Anbananthen, K. (2023). Bert: A review of applications in sentiment analysis. HighTech and Innovation Journal, 4:453–462.
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Werneck, H., Silva, N., Viana, M. C., Mourão, F., Pereira, A. C., and Rocha, L. (2020). A survey on point-of-interest recommendation in location-based social networks. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 185–192.
Wilcoxon, F. (1992). Individual Comparisons by Ranking Methods, pages 196–202. Springer New York, New York, NY.
Wu, H., Guo, G., Yang, E., Luo, Y., Chu, Y., Jiang, L., and Wang, X. (2024). Pesi: Personalized explanation recommendation with sentiment inconsistency between ratings and reviews. Knowledge-Based Systems, 283:111133.
Bittencourt, G., Vasconcelos, N., Andrade, Y., Silva, N., Cunha, W., Colombo Dias, D. R., Gonçalves, M. A., and Rocha, L. (2026). Review-aware recommender systems (rarss): Recent advances, experimental comparative analysis, discussions, and new directions. ACM Comput. Surv., 58(1).
Dang, C., Moreno García, M., and De La Prieta, F. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21:5666.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Burstein, J., Doran, C., and Solorio, T., editors, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186.
Ganu, G., Elhadad, N., and Marian, A. (2009). Beyond the stars: improving rating predictions using review text content. In 12th International Workshop on the Web and Databases (WebDB), pages 1–6.
Jannach, D., Zanker, M., Felfernig, A., and Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
Li, C., Quan, C., Peng, L., Qi, Y., Deng, Y., and Wu, L. (2019). A capsule network for recommendation and explaining what you like and dislike. In Proceedings of the 42nd ACM SIGIR, SIGIR’19.
Li, D., Liu, H., Zhang, Z., Lin, K., Fang, S., Li, Z., and Xiong, N. N. (2021). Carm: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms. Neurocomputing, 455:283–296.
Liu, H., Wang, Y., Peng, Q., Wu, F., Gan, L., Pan, L., and Jiao, P. (2020). Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing, 374:77–85.
Liu, J., Li, T., Yu, M., Yang, S., Tang, Z., and Yang, Z. (2025). A multi-factor collaborative prediction for review-based recommendation. In Proceedings of the Nineteenth ACM RecSys.
Ni, J., Li, J., and McAuley, J. (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Inui, K., Jiang, J., Ng, V., and Wan, X., editors, Proceedings o EMNLP-IJCNLP.
Ricci, F., Rokach, L., and Shapira, B. (2015). Recommender systems: introduction and challenges. Recommender systems handbook, pages 1–34.
Sayeed, M. S., Roji, V., and Anbananthen, K. (2023). Bert: A review of applications in sentiment analysis. HighTech and Innovation Journal, 4:453–462.
Srifi, M., Oussous, A., Ait Lahcen, A., and Mouline, S. (2020). Recommender systems based on collaborative filtering using review texts—a survey. Information, 11(6):317.
Town, N. (2019). bert-base-multilingual-uncased-sentiment.
Werneck, H., Silva, N., Viana, M. C., Mourão, F., Pereira, A. C., and Rocha, L. (2020). A survey on point-of-interest recommendation in location-based social networks. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 185–192.
Wilcoxon, F. (1992). Individual Comparisons by Ranking Methods, pages 196–202. Springer New York, New York, NY.
Wu, H., Guo, G., Yang, E., Luo, Y., Chu, Y., Jiang, L., and Wang, X. (2024). Pesi: Personalized explanation recommendation with sentiment inconsistency between ratings and reviews. Knowledge-Based Systems, 283:111133.
Publicado
08/09/2026
Como Citar
ANDRADE, Yan; VASCONCELOS, Naan; PEREIRA, Adriano; ROCHA, Leonardo.
Quando Comentários e Notas Discordam: Implicações para Sistemas de Recomendação Baseados em Reviews. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 805-811.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249374.
