Sistemas de Recomendação com Filtros Colaborativos: um Estudo Comparativo
Resumo
Sistemas de Recomendação podem ser encontrados todos os dias de diferentes formas e com diferentes finalidades, eles têm sido altamente relevantes para lidar com a sobrecarga de informações que há na Internet. A Filtragem Colaborativa é uma das técnicas mais usadas e bem sucedidas em Sistemas de Recomendação, porém os autores que discorrem sobre o tema, geralmente não abordam o contéudo do ponto de vista comparativo, validando seus resultados em conjuntos de dados particulares. Com isso, faz-se necessário um estudo, com um conjunto de dados representativos a fim de mapear as vantagens e desvantagens de cada método. Neste contexto, este trabalho busca comparar os principais métodos de sistemas de recomendação com filtros colaborativos, com o objetivo de preencher a atual lacuna existente a respeito dessas comparações Diretas.
Palavras-chave:
Sistemas de Recomendação, Filtragem Colaborativa, estudo comparativo, K-Nearest Neighbors, Matrix Factorization, Non-Negative Matrix Factorization, Singular Value Decomposition
Referências
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Badrul Sarwar, George Karypis, J. K. J. R. (2001). Item-based collaborative filtering recommendation algorithms. Proc. Int. Conf. World Wide Web, 4:285–295.
Bell, R., Koren, Y., and Volinsky, C. (2009). Matrix factorization techniques for recom- mender systems. Computer, 42(08):30–37.
Bobadilla, J., Ortega, F., Hernando, A., and Gutie´rrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46:109 – 132.
Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. pages 177–186.
Camacho, L. A. G. and Alves-Souza, S. N. (2018). Social network data to alleviate cold-start in recommender system: A systematic review. Information Processing Ma- nagement, 54(4):529 – 544.
Chen, R., Hua, Q., Chang, Y., Wang, B., Zhang, L., and Kong, X. (2018). A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access, 6:64301–64320.
Dan Yang, Zongming Ma, A. B. (2011). A sparse svd method for high-dimensional data.
Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1):1–12.
Hernando, A., Bobadilla, J., and Ortega, F. (2016). A non negative matrix factorization for collaborative filtering recommender systems based on a bayesian probabilistic model. Knowledge-Based Systems, 97:188 – 202.
HUANG Bo, YAN Xuanhui, L. J. (2016). Collaborative filtering recommendation algo- rithm based on joint nonnegative matrix factorization. 29(6):725–734.
Koohi, H. and Kiani, K. (2017). A new method to find neighbor users that improves the performance of collaborative filtering. Expert Systems with Applications, 83:30 – 39.
Luo, X., Zhou, M., Xia, Y., and Zhu, Q. (2014). An efficient non-negative matrix- factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 10(2):1273–1284.
Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., and Zhou, T. (2012). Re- commender systems. Physics Reports, 519(1):1 – 49. Recommender Systems.
Moradi, P. and Ahmadian, S. (2015). A reliability-based recommendation method to improve trust-aware recommender systems. Expert Systems with Applications, 42(21):7386 – 7398.
Resnick, P. and Hal R. Varian, G. E. (1997). Recommender systems. pages 56+.
Ricci F., Rokach L., S. B. K. P. (2010). Context-aware recommender systems. Recom- mender Systems Handbook, page 217–253.
Salter, J. and Antonopoulos, N. (2006). Cinemascreen recommender agent: combining collaborative and content-based filtering. IEEE Intelligent Systems, 21(1):35–41.
Sherif, N. and Zhang, G. (2017). Collaborative filtering using probabilistic matrix facto- rization and a bayesian nonparametric model. pages 391–396.
Yang, Z., Wu, B., Zheng, K., Wang, X., and Lei, L. (2016). A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access, 4:3273–3287.
Zhou, X., He, J., Huang, G., and Zhang, Y. (2015). Svd-based incremental approaches for recommender systems. Journal of Computer and System Sciences, 81(4):717 – 733.
Badrul Sarwar, George Karypis, J. K. J. R. (2001). Item-based collaborative filtering recommendation algorithms. Proc. Int. Conf. World Wide Web, 4:285–295.
Bell, R., Koren, Y., and Volinsky, C. (2009). Matrix factorization techniques for recom- mender systems. Computer, 42(08):30–37.
Bobadilla, J., Ortega, F., Hernando, A., and Gutie´rrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46:109 – 132.
Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. pages 177–186.
Camacho, L. A. G. and Alves-Souza, S. N. (2018). Social network data to alleviate cold-start in recommender system: A systematic review. Information Processing Ma- nagement, 54(4):529 – 544.
Chen, R., Hua, Q., Chang, Y., Wang, B., Zhang, L., and Kong, X. (2018). A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access, 6:64301–64320.
Dan Yang, Zongming Ma, A. B. (2011). A sparse svd method for high-dimensional data.
Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1):1–12.
Hernando, A., Bobadilla, J., and Ortega, F. (2016). A non negative matrix factorization for collaborative filtering recommender systems based on a bayesian probabilistic model. Knowledge-Based Systems, 97:188 – 202.
HUANG Bo, YAN Xuanhui, L. J. (2016). Collaborative filtering recommendation algo- rithm based on joint nonnegative matrix factorization. 29(6):725–734.
Koohi, H. and Kiani, K. (2017). A new method to find neighbor users that improves the performance of collaborative filtering. Expert Systems with Applications, 83:30 – 39.
Luo, X., Zhou, M., Xia, Y., and Zhu, Q. (2014). An efficient non-negative matrix- factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 10(2):1273–1284.
Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., and Zhou, T. (2012). Re- commender systems. Physics Reports, 519(1):1 – 49. Recommender Systems.
Moradi, P. and Ahmadian, S. (2015). A reliability-based recommendation method to improve trust-aware recommender systems. Expert Systems with Applications, 42(21):7386 – 7398.
Resnick, P. and Hal R. Varian, G. E. (1997). Recommender systems. pages 56+.
Ricci F., Rokach L., S. B. K. P. (2010). Context-aware recommender systems. Recom- mender Systems Handbook, page 217–253.
Salter, J. and Antonopoulos, N. (2006). Cinemascreen recommender agent: combining collaborative and content-based filtering. IEEE Intelligent Systems, 21(1):35–41.
Sherif, N. and Zhang, G. (2017). Collaborative filtering using probabilistic matrix facto- rization and a bayesian nonparametric model. pages 391–396.
Yang, Z., Wu, B., Zheng, K., Wang, X., and Lei, L. (2016). A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access, 4:3273–3287.
Zhou, X., He, J., Huang, G., and Zhang, Y. (2015). Svd-based incremental approaches for recommender systems. Journal of Computer and System Sciences, 81(4):717 – 733.
Publicado
22/11/2019
Como Citar
PINHEIRO, Matheus Franca; DA SILVA, Nádia Félix Felipe.
Sistemas de Recomendação com Filtros Colaborativos: um Estudo Comparativo. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 7. , 2019, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 155-168.
