Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Joao Vinagre (Autor) Outra; Alipio Mario Jorge (Autor) FCUP; Joao Gama (Autor) FEP

Ata de Conferência Internacional

Título: USER MODELING, ADAPTATION, AND PERSONALIZATION, UMAP 2014

Páginas: 459-470

22nd International Conference on User Modeling, Adaptation, and Personalization (UMAP)

Aalborg, DENMARK, JUL 07-11, 2014

Indexação

ISI Web of Knowledge - 58 Citações

Scopus - 67 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-00A-1W7

DOI: 10.1007/978-3-319-08786-3_41

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12