  Collaborative filtering (CF) is an important approach for recommendation
system which is widely used in a great number of aspects of our life, heavily
in the online-based commercial systems. One popular algorithms in CF is the
K-nearest neighbors (KNN) algorithm, in which the similarity measures are used
to determine nearest neighbors of a user, and thus to quantify the dependency
degree between the relative user/item pair. Consequently, CF approach is not
just sensitive to the similarity measure, yet it is completely contingent on
selection of that measure. While Jaccard - as one of those commonly used
similarity measures for CF tasks - concerns the existence of ratings, other
numerical measures such as cosine and Pearson concern the magnitude of ratings.
Particularly speaking, Jaccard is not a dominant measure, but it is long proven
to be an important factor to improve any measure. Therefore, in our continuous
efforts to find the most effective similarity measures for CF, this research
focuses on proposing new similarity measure via combining Jaccard with several
numerical measures. The combined measures would take the advantages of both
existence and magnitude. Experimental results on, Movie-lens dataset, showed
that the combined measures are preeminent outperforming all single measures
over the considered evaluation metrics.
