Descripción
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Recommender systems are highly sensitive to cases of false- positives, that is, recommendations made which have proved not to be relevant. These situations often lead to a loss of trust in the system by the users; therefore, every improvement in the recommendation quality measures is important. Recommender systems which admit an extensive set of values in the votes (usually those which admit more than 5 stars to rate an item) cannot be assessed adequately using precision as a recommendation quality measure; this is due to the fact that the division of the possible values of the votes into just two sets, relevant (true-positive) and not-relevant (false-positive), proves to be too poor and involves the accumulation of values in the not-relevant set. In order to establish a balanced quality measure it is necessary to have access to detailed information on how the cases of false-positives are distributed. This paper provides the mathematical formalism which defines the precision quality measure in recommender systems and its generalization to extended-precision. | |
Internacional
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Si |
JCR del ISI
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No |
Título de la revista
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Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence |
ISSN
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0302-9743 |
Factor de impacto JCR
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Información de impacto
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Volumen
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DOI
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Número de revista
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Desde la página
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433 |
Hasta la página
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442 |
Mes
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SIN MES |
Ranking
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