Reciprocal Rank Using Web Page Popularity

To unravel the effect of the recommending mechanism, we investigate separately RNN-based predictors pool and Tree-based predictors pool because lower individual accuracy makes

Figure 3 also depicts the result by implementing a case where there is no collaboration between the caching nodes, namely no fog scenario. It resulted in slightly better perfor-

S´ark¨ozy, “On the number of partitions of n without a given subsum II,” in Analytic Number Theory (B. Szalay, “On some problems

We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly

We scanned the obtained list for open ports, classified the content of Tor hidden services that provide HTTP(S) ser- vice, and estimated the popularity of obtained onion addresses.

Once a content name reaches locally a Popularity Threshold, the content name is tagged as popular and if the node holds the content it suggests its neighbor nodes to cache it through

Accordingly, in this paper we present the Track Popularity Dataset (TPD) that provides different sources of popular- ity definition ranging from 2004 to 2014, a mapping between

The effect of having few data items responsible for the majority of ratings in a Collaborative Filtering recommendation, and the com- plement of having majority of items responsible