InforLorV4, Hal, Corpus, bibRecord, 002891

Hybridising collaborative filtering and trust-aware recommender systems

Identifieur interne : 002891 ( Hal/Corpus ); précédent : 002890; suivant : 002892

Hybridising collaborative filtering and trust-aware recommender systems

Auteurs : Charif Haydar ; Anne Boyer ; Azim Roussanaly

Source :

RBID : Hal:hal-00679233

English descriptors

mix :
- Recommender systems, Reputation, Trust, Users similarity.

Abstract

Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be deﬁned: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisﬁed users. Collaborative ﬁltering (CF) is the master approach in this domain, but still has some weaknesses especially about coverage. Trust-aware approach is today another promising approach in RS within social environments, whose prediction exceeds the quality of (CF). In this paper we propose several strategies to hybridize those both approaches in order to improve prediction quality, in the term of accuracy and coverage.

Url:

https://hal.inria.fr/hal-00679233

Links to Exploration step

Hal:hal-00679233

Le document en format XML

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<front><div type="abstract" xml:lang="en">Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be deﬁned: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisﬁed users. Collaborative ﬁltering (CF) is the master approach in this domain, but still has some weaknesses especially about coverage. Trust-aware approach is today another promising approach in RS within social environments, whose prediction exceeds the quality of (CF). In this paper we propose several strategies to hybridize those both approaches in order to improve prediction quality, in the term of accuracy and coverage.</div>
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<author role="aut"><persName><forename type="first">Anne</forename>
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<author role="crp"><persName><forename type="first">Azim</forename>
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<abstract xml:lang="en">Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be deﬁned: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisﬁed users. Collaborative ﬁltering (CF) is the master approach in this domain, but still has some weaknesses especially about coverage. Trust-aware approach is today another promising approach in RS within social environments, whose prediction exceeds the quality of (CF). In this paper we propose several strategies to hybridize those both approaches in order to improve prediction quality, in the term of accuracy and coverage.</abstract>
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Hybridising collaborative filtering and trust-aware recommender systems

Hybridising collaborative filtering and trust-aware recommender systems

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