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. © 2011 Springer-Verlag.
CITATION STYLE
Ortega, F., Hernando, A., & Bobadilla, J. (2011). Extended precision quality measure for recommender systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7023 LNAI, pp. 433–442). https://doi.org/10.1007/978-3-642-25274-7_44
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