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Detalhes

Detalhes

  • Nome

    João Vinagre
  • Cluster

    Informática
  • Cargo

    Investigador Auxiliar
  • Desde

    11 janeiro 2010
015
Publicações

2021

Statistically robust evaluation of stream-based recommender systems

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

Publicação
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Abstract

2021

A Hybrid Recommender System for Improving Automatic Playlist Continuation

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

Publicação
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Abstract

2021

Partially Monotonic Learning for Neural Networks

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

Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XIX, IDA 2021

Abstract

2021

Hyperparameter self-tuning for data streams

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

Publicação
INFORMATION FUSION

Abstract

2021

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

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

Software library for stream-based recommender systems

Autor
Fernando André Bezerra Moura Fernandes

Instituição
UP-FEUP

2020

Interpretabilidade em Modelos de Sistemas de Recomendação

Autor
Joana Filipa Vieira Trindade

Instituição
UP-FCUP

2020

Hierarchical recommender systems

Autor
Bruna Raquel Ribeiro Madeira

Instituição
UP-FCUP

2019

Exploiting business knowledge in a recommender system for handset devices

Autor
André Guimarães Rodrigues da Silva

Instituição
UP-FCUP

2019

Software library for stream-based recommender systems

Autor
Fernando André Bezerra Moura Fernandes

Instituição
UP-FEUP