A novel approach of cluster based optimal ranking of clicked URLs using genetic algorithm for effective personalized web search

• Genetic Algorithm (GA) is applied for generating the cluster based optimal ranked clicked URLs for effective Personalized Web Search(PWS).
• The computational overhead associated with the use of GA has no impact on the online performance of the PWS using clustered query sessions.
• The experimental results were compared on the basis of percent improvement in average precision of PWS both with and without optimal ranked clicked URLs over Classic IR in the domain Academics, Entertainment and Sports.
• The statistically verified results show more percent improvement in average precision of PWS with optimal ranked clicked URLs than PWS without optimal ranked clicked URLs over Classic IR.

In this paper a novel approach is proposed for generating the optimal ranked clicked URLs using genetic algorithm (GA) based on clustered web query sessions for effective personalized web search. Experimental study was conducted on the data set of web query sessions captured in the domains academics, entertainment and sports to test the effectiveness of clusterwise optimal ranked clicked URLs for personalized web search (PWS). The results, which are verified statistically shows an improvement in the average precision of the personalized web search based on optimal ranked clicked URLs over both Classic IR and personalized web search without optimal ranked clicked URLs. Thus the effectiveness of personalized web search using optimal ranked clicked URLs is confirmed for better customizing the web search according to the information need of the user.