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Improving Incremental Recommenders with Online Bagging

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams. We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positive-only user feedback, often known as binary ratings. Our results show that online bagging is able to improve accuracy up to 35% over the baseline, with small computational overhead.

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Notes

  1. 1.

    http://www.grouplens.org/data [Jan 2013].

  2. 2.

    https://webscope.sandbox.yahoo.com/catalog.php?datatype=r [Jan 2013].

  3. 3.

    http://last.fm/.

  4. 4.

    http://ocelma.net/MusicRecommendationDataset [Jan 2013].

  5. 5.

    https://rdm.inesctec.pt/dataset/cs-2017-003, file: playlisted_tracks.tsv.

  6. 6.

    http://www.palcoprincipal.com/.

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Acknowledgments

Project “TEC4Growth − Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020” is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). This work is also partially funded by the European Commission through project MAESTRA (Grant no. ICT-2013-612944). We thank Ubbin Labs, Lda. for kindly providing data from Palco Principal.

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Authors and Affiliations

  1. LIAAD - INESC TEC, Porto, Portugal

    João Vinagre, Alípio Mário Jorge & João Gama

  2. Faculty of Sciences, University of Porto, Porto, Portugal

    Alípio Mário Jorge

  3. Faculty of Economics, University of Porto, Porto, Portugal

    João Gama

Authors
  1. João Vinagre
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  2. Alípio Mário Jorge
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  3. João Gama
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Corresponding author

Correspondence to João Vinagre .

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Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Vinagre, J., Jorge, A.M., Gama, J. (2017). Improving Incremental Recommenders with Online Bagging. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_49

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_49

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  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

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