TY - GEN

T1 - Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems

AU - Demir, Gül Nildem

AU - Uyar, A. Sima

AU - Oguducu, Sule

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Evolutionary algorithms

KW - Graph-based clustering

KW - Multiobjective

KW - Sequence clustering

UR - http://www.scopus.com/inward/record.url?scp=34548086523&partnerID=8YFLogxK

U2 - 10.1145/1276958.1277346

DO - 10.1145/1276958.1277346

M3 - Conference contribution

AN - SCOPUS:34548086523

SN - 1595936971

SN - 9781595936974

T3 - Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

SP - 1943

EP - 1950

BT - Proceedings of GECCO 2007

T2 - 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007

Y2 - 7 July 2007 through 11 July 2007

ER -