Applying Optimization Techniques for Enhancing Personalization to Recommend a Web Page

Abstract

Web page recommendation system has been emerging as the most important area in Service computing. Web pages are analyzed and selected for recommendation in order to favor end users while searching for information. Collaborative filtering and content based approaches are two predominant techniques for recommending web pages. Traditional Naive Bayes based probabilistic approach has also shown drastic improvement in achieving personalization during Web page recommendations. However, to improve the accuracy and enhance user satisfaction, we have analyzed optimization techniques such as Ant Colony Optimization and Particle Swarm Optimizations for enhancement of personalization in web search. Here, user profiles comprising of Usage-based and Content-Based attributes are clustered based on similarity in search history. Optimization algorithms are applied to select final web pages from the set of users within the matching cluster. Experiments were carried out with datasets covering 7175 web pages accessed by 287 different users. Result shows that Particle Swarm Optimization outperforms other traditional methods with improved performance.