Computing
similarities between nodes of a graph, with
application to collaborative recommendation
François Fouss
Tuesday 22 November 2005 @ 14:00 - room 2N8.209 - NO
building
Abstract:
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.
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