|
Descripción
|
|
|---|---|
| Recommender systems are typically provided as Web 2.0 services and are part of the range of applica- tions that give support to large-scale social networks, enabling on-line recommendations to be made based on the use of networked databases. The operating core of recommender systems is based on the collaborative filtering stage, which, in current user to user recommender processes, usually uses the Pear- son correlation metric. In this paper, we present a new metric which combines the numerical information of the votes with independent information from those values, based on the proportions of the common and uncommon votes between each pair of users. Likewise, we define the reasoning and experiments on which the design of the metric is based and the restriction of being applied to recommender systems where the possible range of votes is not greater than 5. In order to demonstrate the superior nature of the proposed metric, we provide the comparative results of a set of experiments based on the MovieLens, FilmAffinity and NetFlix databases. In addition to the traditional levels of accuracy, results are also pro- vided on the metrics¿ coverage, the percentage of hits obtained and the precision/recall. | |
|
Internacional
|
Si |
|
JCR del ISI
|
Si |
|
Título de la revista
|
KNOWLEDGE-BASED SYSTEMS |
|
ISSN
|
0950-7051 |
|
Factor de impacto JCR
|
0,924 |
|
Información de impacto
|
|
|
Volumen
|
22 |
|
DOI
|
|
|
Número de revista
|
4 |
|
Desde la página
|
261 |
|
Hasta la página
|
265 |
|
Mes
|
ENERO |
|
Ranking
|
|