| 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 | 209 | 2006 |
| 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 | 125 | 2012 |
| 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 | 111 | 2014 |
| 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 | 70 | 2017 |
| Demand-driven tag recommendation GV Menezes, JM Almeida, F Belém, MA Gonçalves, A Lacerda, ES Moura, ... Joint European conference on machine learning and knowledge discovery in …, 2010 | 51 | 2010 |
| Multi-objective ranked bandits for recommender systems A Lacerda Neurocomputing 246, 12-24, 2017 | 43 | 2017 |
| A video summarization approach based on the emulation of bottom-up mechanisms of visual attention H Jacob, FLC Pádua, A Lacerda, A Pereira Journal of Intelligent Information Systems 49 (2), 193-211, 2017 | 27 | 2017 |
| 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 | 25 | 2013 |
| 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 | 25 | 2011 |
| A robust indoor scene recognition method based on sparse representation G Nascimento, C Laranjeira, V Braz, A Lacerda, ER Nascimento Iberoamerican Congress on Pattern Recognition, 408-415, 2017 | 20 | 2017 |
| 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 | 18 | 2016 |
| 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 |
| Improving daily deals recommendation using explore-then-exploit strategies A Lacerda, RLT Santos, A Veloso, N Ziviani Information Retrieval Journal 18 (2), 95-122, 2015 | 14 | 2015 |
| Multimodal data fusion framework based on autoencoders for top-N recommender systems FLC Pádua, A Lacerda, AC Machado, DH Dalip Applied Intelligence 49 (9), 3267-3282, 2019 | 13 | 2019 |
| Exploratory and interactive daily deals recommendation A Lacerda, A Veloso, N Ziviani Proceedings of the 7th ACM conference on recommender systems, 439-442, 2013 | 13 | 2013 |
| 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 | 12 | 2013 |
| Detecting collaboration profiles in success-based music genre networks GP Oliveira, MO Silva, DB Seufitelli, A Lacerda, MM Moro Procs. Int’l Society for Music Information Retrieval Conference (ISMIR …, 2020 | 10 | 2020 |
| Contextual bandits for multi-objective recommender systems A Lacerda 2015 Brazilian Conference on Intelligent Systems (BRACIS), 68-73, 2015 | 10 | 2015 |
| Using taxonomies for product recommendation O Matos-Junior, N Ziviani, F Botelho, M Cristo, A Lacerda, AS da Silva Journal of Information and Data Management 3 (2), 85-85, 2012 | 10 | 2012 |
| Evaluation of interest point matching methods for projective reconstruction of 3d scenes DN Brito, CFG Nunes, FLC Padua, A Lacerda IEEE Latin America Transactions 14 (3), 1393-1400, 2016 | 8 | 2016 |
