Forgetting techniques for stream-based matrix factorization in recommender systems

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Matuszyk, P (Autor) Outra; Vinagre, J (Autor) Outra; Spiliopoulou, M (Autor) Outra; Jorge, AM (Autor) FCUP; João Gama (Autor) FEP

Revista

Título: Knowledge and Information Systems

Vol. 55

Páginas: 275-304

ISSN: 0219-1377

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 17 Citações

Scopus - 21 Citações

Outras Informações

ID Authenticus: P-00N-R6Y

DOI: 10.1007/s10115-017-1091-8

Abstract (EN): Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users' preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users' preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information significantly improves predictive power of recommender systems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 30