SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Diego da Silva(diego.correa@ufba.br)
2 Joel Pires(joelpires@ufba.br)
3 Frederico Durão(fdurao@ufba.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Abdollahpouri, H., Mansoury, M., Burke, R., and Mobasher, B. (2020). The connection between popularity bias, calibration, and fairness in recommendation. In Fourteenth ACM Conference on Recommender Systems, RecSys ’20, page 726–731, New York, NY, USA. Association for Computing Machinery.
2 Abdollahpouri, H., Nazari, Z., Gain, A., Gibson, C., Dimakopoulou, M., Anderton, J., Carterette, B., Lalmas, M., and Jebara, T. (2023). Calibrated recommendations as a minimum-cost flow problem. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM ’23, page 571–579, New York, NY, USA. Association for Computing Machinery.
3 Aragão, L. R., Silva, M., and Machado, J. (2024). The inefficiency of achieving fairness with protected attribute suppression. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 813–819, Porto Alegre, RS, Brasil. SBC.
4 Carbonell, J. and Goldstein, J. (1998). The use of mmr, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’98, pages 335–336, New York, NY, USA. ACM
5 Cha, S.-H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Model. Meth. Appl. Sci., 1.
6 da Silva, D. C. and Durão, F. A. (2023a). How novel and unexpected the calibrated recommendations are? In American Conference on Information Systems, volume 6
7 da Silva, D. C. and Durão, F. A. (2023b). Introducing a framework and a decision protocol to calibrated recommender systems. Applied Intelligence.
8 da Silva, D. C. and Durão, F. A. (2025). Benchmarking fairness measures for calibrated recommendation systems on movies domain. Expert Systems with Applications, 269:126380.
9 da Silva, D. C., Jannach, D., and Durão, F. A. (2025). Considering time and feature entropy in calibrated recommendations. ACM Trans. Intell. Syst. Technol. Just Accepted.
10 da Silva, D. C., Manzato, M. G., and Durão, F. A. (2021). Exploiting personalized calibration and metrics for fairness recommendation. Expert Systems with Applications, page 115112.
11 Desrosiers, C. and Karypis, G. (2011). A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook
12 Hug, N. (2017). Surprise, a Python library for recommender systems. http://surpriselib.com.
13 Jolad, S., Roman, A., Shastry, M. C., Gadgil, M., and Basu, A. (2016). A new family of bounded divergence measures and application to signal detection. Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
14 Kaya, M. and Bridge, D. (2019). A comparison of calibrated and intent-aware recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys ’19, page 151–159, New York, NY, USA. Association for Computing Machinery.
15 Koren, Y. and Bell, R. (2015). Advances in Collaborative Filtering, pages 77–118. Springer.
16 Leite, N., Campelo, C. E. C., and Silva, S. D. (2024). Leveraging geographic feature embeddings for enhanced location-based recommendation systems. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 354–366, Porto Alegre, RS, Brasil. SBC.
17 Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., and Glance, N. (2007). Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 420–429.
18 Liang, D., Altosaar, J., Charlin, L., and Blei, D. M. (2016). Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, page 59–66, New York, NY, USA. Association for Computing Machinery.
19 Lin, K., Sonboli, N., Mobasher, B., and Burke, R. (2020). Calibration in collaborative filtering recommender systems: A user-centered analysis. In Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT ’20, page 197–206, New York, NY, USA. Association for Computing Machinery
20 Nemhauser, G. L.,Wolsey, L. A., and Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions–i. Math. Program., 14(1):265–294
21 Parra, D. and Sahebi, S. (2013). Recommender systems: Sources of knowledge and evaluation metrics. In Advanced techniques in web intelligence-2, pages 149–175. Springer.
22 Sena, L. and Machado, J. (2024). Evaluation of fairness in machine learning models using the uci adult dataset. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 743–749, Porto Alegre, RS, Brasil. SBC
23 Steck, H. (2018). Calibrated recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys ’18, pages 154–162, New York, NY, USA. ACM.
24 Zhao, X., Zhu, Z., Alfifi, M., and Caverlee, J. (2020). Addressing the target customer distortion problem in recommender systems. In Proceedings of The Web Conference 2020, WWW ’20, page 2969–2975, New York, NY, USA. Association for Computing Machinery.