LATIN has contributed for research in the following areas:
Information Retrieval Models and Evaluation
Ranking algorithms; learning-to-rank; retrieval models; search result diversification; entity-based search; query understanding; evaluation metrics.
Recommender Systems
Content-based recommender system; collaborative filtering; Context-based recommendation; personalized recommendation.
Digital Libraries
Collection discovery and development; knowledge discovery; applications of machine learning and AI; bibliometrics; expert search.
Recent Projects
National Institute of Science and Technology for a Massively Connected Society
2016 - Present
National Institutes of Science and Technology (INCT).
Contextual Information Retrieval
2016 - Present
Research Productivity - PQ 2015.
Contextual Recommendation in Local-based Social Networks
2016 - Present
Funding Internal for Scientific Initiation (PROBIC/FAPEMIG) UFMG/PRPQ 03/2015.
Proactive Recommendation on Mobile Devices
2016 - Present
Funding Internal for Scientific Initiation (PROBIC/FAPEMIG) UFMG/PRPQ 01/2016.
Contextual Recommendation on Mobile Devices
2015 - Present
Internal funding to recently hired professors of UFMG (ADRC) UFMG/PRPQ 14/2013.
Information Retrieval in Academic Social Networks
2015 - Present
Microsoft Azure for Research Award.
MasWeb - Models, Algorithms, and Web Systems
2015 - Present
Funding for Support of Research excellence (PRONEX) FAPEMIG 19/2013.
Semantic Information Retrieval
2014 - 2015
Internal funding for Scientific Initiation. (PIBIC/CNPq) UFMG/PRPQ 02/2014.