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Detalhes

Detalhes

  • Nome

    João Vinagre
  • Cluster

    Informática
  • Cargo

    Investigador Auxiliar
  • Desde

    11 janeiro 2010
004
Publicações

2018

Online bagging for recommender systems

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

Publicação
Expert Systems

Abstract

2018

Online Gradient Boosting for Incremental Recommender Systems

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

Publicação
Discovery Science - Lecture Notes in Computer Science

Abstract

2015

An overview on the exploitation of time in collaborative filtering

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

Publicação
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user-generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the systemand old ones leavinguser and item activity rate fluctuations and other similar time-related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future.(C) 2015 John Wiley & Sons, Ltd.

2014

Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

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

Publicação
USER MODELING, ADAPTATION, AND PERSONALIZATION, UMAP 2014

Abstract
Traditional Collaborative Filtering algorithms for recommendation are designed for stationary data. Likewise, conventional evaluation methodologies are only applicable in offline experiments, where data and models are static. However, in real world systems, user feedback is continuously being generated, at unpredictable rates. One way to deal with this data stream is to perform online model updates as new data points become available. This requires algorithms able to process data at least as fast as it is generated. One other issue is how to evaluate algorithms in such a streaming data environment. In this paper we introduce a simple but fast incremental Matrix Factorization algorithm for positive-only feedback. We also contribute with a prequential evaluation protocol for recommender systems, suitable for streaming data environments. Using this evaluation methodology, we compare our algorithm with other state-of-the-art proposals. Our experiments reveal that despite its simplicity, our algorithm has competitive accuracy, while being significantly faster.