| Learning to advertise A Lacerda, M Cristo, MA Gonçalves, W Fan, N Ziviani, B Ribeiro-Neto Proceedings of the 29th annual international ACM SIGIR conference on …, 2006 | 219 | 2006 |
| Multiobjective pareto-efficient approaches for recommender systems MT Ribeiro, N Ziviani, ESD Moura, I Hata, A Lacerda, A Veloso ACM Transactions on Intelligent Systems and Technology (TIST) 5 (4), 1-20, 2014 | 162 | 2014 |
| Pareto-efficient hybridization for multi-objective recommender systems MT Ribeiro, A Lacerda, A Veloso, N Ziviani Proceedings of the sixth ACM conference on Recommender systems, 19-26, 2012 | 159 | 2012 |
| A general framework to expand short text for topic modeling P Bicalho, M Pita, G Pedrosa, A Lacerda, GL Pappa Information Sciences 393, 66-81, 2017 | 93 | 2017 |
| Demand-driven tag recommendation GV Menezes, JM Almeida, F Belém, MA Gonçalves, A Lacerda, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010 | 55 | 2010 |
| Multi-objective ranked bandits for recommender systems A Lacerda Neurocomputing 246, 12-24, 2017 | 52 | 2017 |
| A video summarization approach based on the emulation of bottom-up mechanisms of visual attention H Jacob, FLC Pádua, A Lacerda, ACM Pereira Journal of Intelligent Information Systems 49, 193-211, 2017 | 36 | 2017 |
| Detecting Collaboration Profiles in Success-based Music Genre Networks. GP Oliveira, M Santos, DB Seufitelli, A Lacerda, MM Moro ISMIR, 726-732, 2020 | 29 | 2020 |
| Is rank aggregation effective in recommender systems? an experimental analysis SEL Oliveira, V Diniz, A Lacerda, L Merschmanm, GL Pappa ACM Transactions on Intelligent Systems and Technology (TIST) 11 (2), 1-26, 2020 | 28 | 2020 |
| Minimal perfect hashing: A competitive method for indexing internal memory FC Botelho, A Lacerda, GV Menezes, N Ziviani Information Sciences 181 (13), 2608-2625, 2011 | 26 | 2011 |
| A robust indoor scene recognition method based on sparse representation G Nascimento, C Laranjeira, V Braz, A Lacerda, ER Nascimento Progress in Pattern Recognition, Image Analysis, Computer Vision, and …, 2018 | 25 | 2018 |
| Building user profiles to improve user experience in recommender systems A Lacerda, N Ziviani Proceedings of the sixth ACM international conference on Web search and data …, 2013 | 23 | 2013 |
| Topic modeling for short texts with co-occurrence frequency-based expansion G Pedrosa, M Pita, P Bicalho, A Lacerda, GL Pappa 2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 277-282, 2016 | 21 | 2016 |
| On modeling context from objects with a long short-term memory for indoor scene recognition C Laranjeira, A Lacerda, ER Nascimento 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI …, 2019 | 16 | 2019 |
| Multimodal data fusion framework based on autoencoders for top-N recommender systems FLA Conceiç ao, FLC Pádua, A Lacerda, AC Machado, DH Dalip Applied Intelligence 49, 3267-3282, 2019 | 16 | 2019 |
| Weighted slope one predictors revisited D Menezes, A Lacerda, L Silva, A Veloso, N Ziviani Proceedings of the 22nd international conference on world wide web, 967-972, 2013 | 16 | 2013 |
| Guard: A genetic unified approach for recommendation A Guimarães, TF Costa, A Lacerda, GL Pappa, N Ziviani Journal of Information and Data Management 4 (3), 295-295, 2013 | 15 | 2013 |
| Individualized extreme dominance (IndED): A new preference-based method for multi-objective recommender systems RS Fortes, DX de Sousa, DG Coelho, AM Lacerda, MA Gonçalves Information Sciences 572, 558-573, 2021 | 14 | 2021 |
| Explaining symbolic regression predictions R Miranda Filho, A Lacerda, GL Pappa 2020 IEEE congress on evolutionary computation (CEC), 1-8, 2020 | 14 | 2020 |
| Improving daily deals recommendation using explore-then-exploit strategies A Lacerda, RLT Santos, A Veloso, N Ziviani Information Retrieval Journal 18, 95-122, 2015 | 14 | 2015 |
Gisele L. PappaComputer Science, Universidade Federal de Minas Gerais在 dcc.ufmg.br 的电子邮件经过验证
