Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/4235
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dc.contributor.authorJoão Marques Silvaen
dc.contributor.authorAlípio Jorgeen
dc.contributor.authorJoão Gamaen
dc.date.accessioned2017-12-18T17:02:59Z-
dc.date.available2017-12-18T17:02:59Z-
dc.date.issued2017en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/4235-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-65340-2_49en
dc.description.abstractOnline 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. © Springer International Publishing AG 2017.en
dc.languageengen
dc.relation5120en
dc.relation5245en
dc.relation4981en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleImproving Incremental Recommenders with Online Baggingen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:LIAAD - Articles in International Conferences

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