Sentence examples for a recommendation class from inspiring English sources

where x i is a recommendation class from a recommendation set x.

Since median is resistent to outlier, we have proposed a dissimilarity function that captures how dissimilar a recommendation class is from the median of the recommendation set.

Therefore, the recommendation (class I, level of evidence A) exists for a weight reduction of overweight or obese individuals who have not undergone any cardiovascular event.

In Table2 the recommendation class R c5 has the highest deviation value, so it is taken as a suspicious recommendation class and is added to the suspicious recommendation domain (SRdomain), and its SF is calculated.

After arranging the recommendations in their respective recommendation class R c i, we remove the recommendation classes with zero frequencies and calculate DF(R c i ) for each recommendation class using Equation 1. Table2 shows the sorted list of recommendation classes with respect to their dissimilarity value.

Let R c k be the kth recommendation class of R domain and SRdomain be the set of suspicious recommendation classes from R domain, i.e., SRdomain ⊆ R domain.

where k = 1,2,3…,m − 1, and m is the distinct recommendation class value number in sorted R domain.

In order to find out the set of dishonest recommendation R domaindishonest from R domain, the mechanism defined by the proposed approach is as follows:  Let R c k be the kth recommendation class of R domain and SRdomain be the set of suspicious recommendation classes from R domain, i.e., SRdomain ⊆ R domain.

The recommendation class at the top of the sorted R domain with respect to its DF(x j ) is considered to be the most suspicious one to be filtered out as dishonest recommendation.

Initially, SRdomain is an empty set, SRdomain0 =  Compute SF SRdomain k ) for each SRdomain k formed by taking the union of SRdomaink−1 and R c k. SRdomain k = SRdomain k − 1 ∪ R c k (3)  where k = 1,2,3…,m − 1, and m is the distinct recommendation class value number in sorted R domain.

Next we take the union of the suspicious recommendation domain SRdomain1 and the next recommendation class in the sorted list, i.e., R c4 and calculate its SF using Equation 2. This process is repeated for each R c i of R domain until SRdomain = R domain−R c m, where m = 5.