Citation
Bobadilla Sancho, Jesus and Ortega Requena, Fernando and Hernando Esteban, Antonio and Arroyo Castillo, Angel
(2012).
A Balanced Memory-Based Collaborative Filtering Similarity Measure..
"International Journal of Intelligent Systems", v. 27
;
pp. 939-946.
ISSN 0884-8173.
https://doi.org/10.1002/int.21556.
Abstract
Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.