Anisio Lacerda
Cited by
Cited by
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
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
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
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
Demand-driven tag recommendation
GV Menezes, JM Almeida, F Belém, MA Gonçalves, A Lacerda, ...
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010
Multi-objective ranked bandits for recommender systems
A Lacerda
Neurocomputing 246, 12-24, 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
Detecting Collaboration Profiles in Success-based Music Genre Networks.
GP Oliveira, M Santos, DB Seufitelli, A Lacerda, MM Moro
ISMIR, 726-732, 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
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
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
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
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
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
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
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
Faster and slower posttraining recovery in futsal: multifactorial classification of recovery profiles
CF Wilke, FAP Fernandes, FVC Martins, AM Lacerda, FY Nakamura, ...
International journal of sports physiology and performance 14 (8), 1089-1095, 2019
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
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
Explaining symbolic regression predictions
R Miranda Filho, A Lacerda, GL Pappa
2020 IEEE congress on evolutionary computation (CEC), 1-8, 2020
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Articles 1–20