Anzeige
Erschienen in:
2007 | OriginalPaper | Buchkapitel
Comparing State-of-the-Art Collaborative Filtering Systems
verfasst von : Laurent Candillier, Frank Meyer, Marc Boullé
Erschienen in: Machine Learning and Data Mining in Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Collaborative filtering
aims at helping
users
find
items
they should appreciate from huge catalogues. In that field, we can distinguish
user-based
,
item-based
and
model-based
approaches. For each of them, many options play a crucial role for their performances, and in particular the similarity function defined between users or items, the number of neighbors considered for user- or item-based approaches, the number of clusters for model-based approaches using clustering, and the prediction function used.
In this paper, we review the main collaborative filtering methods proposed in the litterature and compare them on the same widely used real dataset called
MovieLens
, and using the same widely used performance measure called
Mean Absolute Error
(MAE). This study thus allows us to highlight the advantages and drawbacks of each approach, and to propose some default options that we think should be used when using a given approach or designing a new one.