Improving Incremental Recommenders with Online Bagging

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Vinagre, J (Autor) Outra; Jorge, AM (Autor) FCUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Páginas: 597-607

18th EPIA Conference on Artificial Intelligence (EPIA)

Univ Porto, Fac Engn, Porto, PORTUGAL, SEP 05-08, 2017

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00M-YEW

DOI: 10.1007/978-3-319-65340-2_49

Abstract (EN): 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 positiveonly 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11