Graph-based Sequence Clustering through Multiobjective Evolutionary Algorithms for Web Recommender Systems

In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends oil the quality, Of this clustering solution. lit these types of applications, data to be clustered is in the form of user sessions which area 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 session,, 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 oil sample data sets and the most suitable approach is determined.