Scale And Translation Invariant Collaborative Filtering Systems

Lemire, Daniel (2005). « Scale And Translation Invariant Collaborative Filtering Systems ». Information Retrieval, 8(1), pp. 129-150.

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Résumé

Collaborative filtering systems are prediction algorithms over sparse data sets of user preferences. We modify a wide range of state-of-the-art collaborative filtering systems to make them scale and translation invariant and generally improve their accuracy without increasing their computational cost. Using the EachMovie and the Jester data sets, we show that learning-free constant time scale and translation invariant schemes outperforms other learning-free constant time schemes by at least 3% and perform as well as expensive memory-based schemes (within 4%). Over the Jester data set, we show that a scale and translation invariant Eigentaste algorithm outperforms Eigentaste 2.0 by 20%. These results suggest that scale and translation invariance is a desirable property.

Type:	Article de revue scientifique
Mots-clés ou Sujets:	Recommender System, Incomplete Vectors, Regression, e-Commerce
Unité d'appartenance:	Télé-université > UER Science et Technologie
Déposé par:	Daniel Lemire
Date de dépôt:	05 juin 2007
Dernière modification:	01 nov. 2014 02:03
Adresse URL :	http://archipel.uqam.ca/id/eprint/318

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