Detalhes
Nome
João VinagreCluster
InformáticaCargo
Investigador AuxiliarDesde
11 janeiro 2010
Nacionalidade
PortugalCentro
Laboratório de Inteligência Artificial e Apoio à DecisãoContactos
+351220402963
joao.m.silva@inesctec.pt
2021
Autores
Vinagre, J; Jorge, AM; Rocha, C; Gama, J;
Publicação
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Abstract
2021
Autores
Gatzioura, A; Vinagre, J; Jorge, AM; Sanchez Marre, M;
Publicação
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Abstract
2021
Autores
Trindade, J; Vinagre, J; Fernandes, K; Paiva, N; Jorge, A;
Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XIX, IDA 2021
Abstract
2021
Autores
Veloso, B; Gama, J; Malheiro, B; Vinagre, J;
Publicação
INFORMATION FUSION
Abstract
2021
Autores
Vinagre, J; Jorge, AM; Al Ghossein, M; Bifet, A;
Publicação
15TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS 2021)
Abstract
Modern online services continuously generate data at very fast rates. This continuous flow of data encompasses content-e.g. posts, news, products, comments-, but also user feedback-e.g. ratings, views, reads, clicks-, together with context data-user device, spacial or temporal data, user task or activity, weather. This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate online methods able to transparently adapt to the inherent dynamics of online services. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with the continuous flows of data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, and their implications regarding multiple dimensions, such as evaluation, reproducibility, privacy and explainability. © 2021 Owner/Author.
Teses supervisionadas
2020
Autor
Fernando André Bezerra Moura Fernandes
Instituição
UP-FEUP
2020
Autor
Joana Filipa Vieira Trindade
Instituição
UP-FCUP
2020
Autor
Bruna Raquel Ribeiro Madeira
Instituição
UP-FCUP
2019
Autor
André Guimarães Rodrigues da Silva
Instituição
UP-FCUP
2019
Autor
Fernando André Bezerra Moura Fernandes
Instituição
UP-FEUP
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