Forgetting mechanisms for scalable collaborative filtering

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Vinagre, J (Autor) Outra; Jorge, AM (Autor) FCUP

Revista

Título: Journal of the Brazilian Computer Society

Vol. 18

Páginas: 271-282

ISSN: 0104-6500

Editora: Springer Nature

Indexação

Scopus - 31 Citações

INSPEC

Outras Informações

ID Authenticus: P-008-6M1

DOI: 10.1007/s13173-012-0077-3

Resumo (PT): Collaborative filtering (CF) has been an important subject of research in the past few years. Many achievements have been made in this field, however, many challenges still need to be faced, mainly related to scalability and predictive ability. One important issue is how to deal with old and potentially obsolete data in order to avoid unnecessary memory usage and processing time. Our proposal is to use forgetting mechanisms. In this paper, we present and evaluate the impact of two forgetting mechanisms—sliding windows and fading factors—in user-based and item-based CF algorithms with implicit binary ratings under a scenario of abrupt change. Our results suggest that forgetting mechanisms reduce time and space requirements, improving scalability, while not significantly affecting the predictive ability of the algorithms.

Abstract (EN): Collaborative filtering (CF) has been an important subject of research in the past few years. Many achievements have been made in this field, however, many challenges still need to be faced, mainly related to scalability and predictive ability. One important issue is how to deal with old and potentially obsolete data in order to avoid unnecessary memory usage and processing time. Our proposal is to use forgetting mechanisms. In this paper, we present and evaluate the impact of two forgetting mechanisms-sliding windows and fading factors-in user-based and item-based CF algorithms with implicit binary ratings under a scenario of abrupt change. Our results suggest that forgetting mechanisms reduce time and space requirements, improving scalability, while not significantly affecting the predictive ability of the algorithms. © 2012 The Brazilian Computer Society.

Idioma: Inglês

Tipo (Avaliação Docente): Científica