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 -