Paper Info
Title
Using tf-idf as an edge weighting scheme in user-object bipartite networks.
Abstract
Bipartite user-object networks are becoming increasingly popular in representing user interaction data in a web or e-commerce environment. They have certain characteristics and challenges that differentiates them from other bipartite networks. This paper analyzes the properties of five real world user-object networks. In all cases we found a heavy tail object degree distribution with popular objects connecting together a large part of the users causing significant edge inflation in the projected users network. We propose a novel edge weighting strategy based on tf-idf and show that the new scheme improves both the density and the quality of the community structure in the projections. The improvement is also noticed when comparing to partially random networks.
Year
Venue
Field
2013
CoRR
Data mining,Weighting,tf–idf,Computer science,Bipartite graph,Theoretical computer science,Degree distribution,Heavy-tailed distribution,Artificial intelligence,Inflation,Machine learning
DocType
Volume
Citations 
Journal
abs/1308.6118
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Sorin Alupoaie130.78
Pádraig Cunningham23086218.37