Solving the apparent diversity-accuracy dilemma of recommender systems.
Proc Natl Acad Sci U S A
; 107(10): 4511-5, 2010 Mar 09.
Article
em En
| MEDLINE
| ID: mdl-20176968
ABSTRACT
Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to address the challenge of diversity and show how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm. By tuning the hybrid appropriately we are able to obtain, without relying on any semantic or context-specific information, simultaneous gains in both accuracy and diversity of recommendations.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Interface Usuário-Computador
/
Armazenamento e Recuperação da Informação
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article