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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems
verfasst von : Laurent Candillier, Frank Meyer, Françoise Fessant
Erschienen in: Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Verlag: Springer Berlin Heidelberg
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The aim of
collaborative filtering
is to help
users
to find
items
that they should appreciate from huge catalogues. In that field, we can distinguish
user-based
from
item-based
approaches. The former is based on the notion of user neighbourhoods while the latter uses item neighbourhoods.
The definition of
similarity
between users and items is a key problem in both approaches. While traditional similarity measures can be used, we will see in this paper that bespoke ones, that are tailored to type of data that is typically available (i.e. very sparse), tend to lead to better results.
Extensive experiments are conducted on two publicly available datasets, called
MovieLens
and
Netflix
. Many similarity measures are compared. And we will show that using weighted similarity measures significantly improves the results of both user- and item-based approaches.