A Novel Way of Computing Similarities between Nodes of a Graph, with Application to Collaborative Recommendation

This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markov-chain model of random walk through the database. The suggested quantities, representing dissimilarities (or similar...

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Vydané v:IEEE/WIC/ACM International Conference on web intelligence s. 550 - 556
Hlavní autori: Fouss, Francois, Pirotte, Alain, Saerens, Marco
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Washington, DC, USA IEEE Computer Society 19.09.2005
IEEE
Edícia:ACM Conferences
Predmet:
Collaboration
Collaborative work
Computing methodologies > Machine learning
Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Cluster analysis
Image databases
Information systems
Information systems > Information systems applications > Data mining
Joining processes
Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms
Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks
Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes
Motion pictures
Navigation
Relational databases
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
Watches
Web mining
ISBN:076952415X, 9780769524153
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Popis
Shrnutí:This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted, undirected, graph. It is based on a Markov-chain model of random walk through the database. The suggested quantities, representing dissimilarities (or similarities) between any two elements, have the nice property of decreasing (increasing) when the number of paths connecting those elements increases and when the "length" of any path decreases. The model is evaluated on a collaborative recommendation task where suggestions are made about which movies people should watch based upon what they watched in the past. The model, which nicely fits into the so-called "statistical relational learning" framework as well as the "link analysis" paradigm, could also be used to compute document or word similarities, and, more generally, could be applied to other database or web mining tasks.
Bibliografia:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:076952415X
9780769524153
DOI:10.1109/WI.2005.9