Google's PageRank method was developed to evaluate the importance of web-pages via
their link structure. The mathematics of PageRank, however, are entirely general and apply …

This paper introduces PerDif; a novel framework for learning personalized diffusions over
item-to-item graphs for top-n recommendation. PerDif learns the teleportation probabilities of …

The web ranking is an essential information to measure the quality of service of a web page.
The dynamic changes in the web information need an efficient framework to rank the web …

More often, the user likes to visit the web documents that appear in few top excellent
responses to the list of links provided by the search engine and these results are the most …

Equip** an imaginary Random Surfer of the Web with the ability to teleport, was Page et
al.'s creative way to justify a mathematical necessity; the Teleportation Matrix. Despite being …

A nonlinear generalisation of the PageRank problem involving the Moore-Penrose inverse
of the incidence matrix is developed for local graph partitioning purposes. The Levenberg …

The frequent utilization of websites has captivated numerous researchers, who have sought
to upgrade the performance of websites through behavior analysis. Weblog feature …

Making recommendations in the presence of sparsity is known to present one of the most
challenging problems faced by collaborative filtering methods. In this work we tackle this …

In this paper, a new ranking technique is introduced to rank Web pages categorically. Web
pages are categorized asPrimary'andSecondary'. It is considered that available Web page …

This project presents a way for measuring the semantic relatedness between concepts in
Knowledge Graphs (KGs) such as WordNet and DBpedia. Past work on semantic similarity …