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About

I'm a researcher at LIAAD, the Laboratory of Artificial Intelligence and Decision Support at INESC TEC. I received my PhD from the Faculty of Sciences of the University of Porto in 2016. My research interests are recommender systems, user modeling, personalization, web intelligence and information retrieval. I'm also interested in more fundamental areas of artificial intelligence, such as data stream mining, neural networks and representation learning. 

Interest
Topics
Details

Details

  • Name

    João Vinagre
  • Cluster

    Computer Science
  • Role

    Assistant Researcher
  • Since

    11th January 2010
015
Publications

2021

Statistically robust evaluation of stream-based recommender systems

Authors
Vinagre, J; Jorge, AM; Rocha, C; Gama, J;

Publication
IEEE Transactions on Knowledge and Data Engineering

Abstract

2021

A Hybrid Recommender System for Improving Automatic Playlist Continuation

Authors
Gatzioura, A; Vinagre, J; Jorge, AM; Sanchez Marre, M;

Publication
IEEE Transactions on Knowledge and Data Engineering

Abstract

2021

Partially Monotonic Learning for Neural Networks

Authors
Trindade, J; Vinagre, J; Fernandes, K; Paiva, N; Jorge, A;

Publication
Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26-28, 2021, Proceedings

Abstract

2021

Hyperparameter self-tuning for data streams

Authors
Veloso, B; Gama, J; Malheiro, B; Vinagre, J;

Publication
Information Fusion

Abstract

2021

ORSUM 2021 - 4th Workshop on Online Recommender Systems and User Modeling

Authors
Vinagre, J; Jorge, AM; Al Ghossein, M; Bifet, A;

Publication
RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021 - 1 October 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.

Supervised
thesis

2021

Mining Causal Links Between Real-World Events and TV Content Viewing Patterns

Author
Orlando Duarte Rodrigues Ferreira de Melo

Institution
UP-FCUP

2021

AutoFITS: Automated feature engineering for irregular time-series

Author
Pedro Miguel Pinto Costa

Institution
UP-FCUP

2021

Online ensembles of local recommendation models

Author
Lúcia Raquel Brandão Moreira

Institution
UP-FCUP

2020

Software library for stream-based recommender systems

Author
Fernando André Bezerra Moura Fernandes

Institution
UP-FEUP

2020

Interpretabilidade em Modelos de Sistemas de Recomendação

Author
Joana Filipa Vieira Trindade

Institution
UP-FCUP