Estudo Comparativo de Bibliotecas para Sistemas de Recomendação em Python
Resumo
Este artigo apresenta uma análise de bibliotecas em linguagem Python que implementam algoritmos de Filtragem Colaborativa usados em Sistemas de Recomendação. Por meio de duas bibliotecas, Surprise e LensKit, são explorados os algoritmos K-Nearest Neighbors (K-NN) e Slope One e realizados testes comparativos para avaliar as bibliotecas.Referências
Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6):734–749.
Ekstrand, M. D. (2020). Lenskit for python: Next-generation software for recommender systems experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management.
Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19.
Hug, N. (2020). Surprise: A python library for recommender systems. Journal of Open Source Software, 5(52):2174.
Lemire, D. and Maclachlan, A. (2005). Slope one predictors for online rating-based collaborative filtering. In Proceedings of the 2005 SIAM International Conference on Data Mining, pages 471–475. SIAM.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pages 175–186.
Ricci, F., Rokach, L., and Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.
Said, A. and Bellogín, A. (2014). Comparative recommender system evaluation: benchmarking recommendation frameworks. In Proceedings of the 8th ACM Conference on Recommender systems, pages 129–136.
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.
Ekstrand, M. D. (2020). Lenskit for python: Next-generation software for recommender systems experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management.
Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19.
Hug, N. (2020). Surprise: A python library for recommender systems. Journal of Open Source Software, 5(52):2174.
Lemire, D. and Maclachlan, A. (2005). Slope one predictors for online rating-based collaborative filtering. In Proceedings of the 2005 SIAM International Conference on Data Mining, pages 471–475. SIAM.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pages 175–186.
Ricci, F., Rokach, L., and Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.
Said, A. and Bellogín, A. (2014). Comparative recommender system evaluation: benchmarking recommendation frameworks. In Proceedings of the 8th ACM Conference on Recommender systems, pages 129–136.
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.
Publicado
23/04/2025
Como Citar
DANESI, Lorenzo Dalla Corte; LICHTNOW, Daniel.
Estudo Comparativo de Bibliotecas para Sistemas de Recomendação em Python. In: ESCOLA REGIONAL DE BANCO DE DADOS (ERBD), 20. , 2025, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 157-160.
ISSN 2595-413X.
DOI: https://doi.org/10.5753/erbd.2025.6883.
