Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems

Gül Nildem Demir*, A. Sima Uyar, Sule Oguducu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Citations (Scopus)

Abstract

In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends on the quality of this clustering solution. In these types of applications, data to be clustered is in the form of user sessions which are sequences of web pages visited by the user. Sequence clustering is one of the important tools to work with this type of data. One way to represent sequence data is through weighted, undirected graphs where each sequence is a vertex and the pairwise similarities between the user sessions are the edges. Through this representation, the problem becomes equivalent to graph partitioning which is NP-complete and is best approached using multiple objectives. Hence it is suitable to use multiobjective evolutionary algorithms (MOEA) to solve it. The main focus of this paper is to determine an effective MOEA to cluster sequence data. Several existing approaches in literature are compared on sample data sets and the most suitable approach is determined.

Original languageEnglish
Title of host publicationProceedings of GECCO 2007
Subtitle of host publicationGenetic and Evolutionary Computation Conference
Pages1943-1950
Number of pages8
DOIs
Publication statusPublished - 2007
Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
Duration: 7 Jul 200711 Jul 2007

Publication series

NameProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Conference

Conference9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
Country/TerritoryUnited Kingdom
CityLondon
Period7/07/0711/07/07

Keywords

  • Evolutionary algorithms
  • Graph-based clustering
  • Multiobjective
  • Sequence clustering

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