Multi-objective Optimization for Adaptive Web Site Generation

Abstract

Designing web sites is a complex problem. Adaptive sites are those which improve themselves by learning from user access patterns. In this paper we have considered a problem of index page synthesis for an adaptive website and framed it in a new type of Multi-Objective Optimization problem. We give a solution to index page synthesis which uses a popular clustering algorithm DBSCAN alongwith NSGA-II–an evolutionary algorithm–to find out best index pages for a website. Our experiments shows that very good candidate index pages can be generated automatically, and that our technique outperforms various existing methods such as PageGather, K-Means and Hierarchical Agglomerative Clustering.