A Hybrid Web Personalization Model Based on Site Connectivity
Miki Nakagawa, Bamshad Mobasher · 2003
Web usage mining has been used effectively as an underlying mechanism for Web personalization and recommender systems. A variety of recommendation frameworks have been proposed, including some based on non-sequential models, such as association rules and clusters, and some based on sequential models, such as sequential or navigational patterns. Our recent studies have suggested that the structural characteristics of Web sites, such as the site topology and the degree of connectivity, have a significant impact on the relative performance of recommendation models based on association rules, contiguous and non-contiguous sequential patterns. In this paper, we present a framework for a hybrid Web personalization system that can intelligently switch among different recommendation models, based on the degree of connectivity and the current location of the user within the site. We have conducted a detailed evaluation based on real Web usage data from three sites with different structural characteristics. Our results show that the hybrid system selects less constrained models such as frequent itemsets when the user is navigating portions of the site with a higher degree of connectivity, while sequential recommendation models are chosen for deeper navigational depths and lower degrees of connectivity. The comparative evaluation also indicates that the overall performance of hybrid system in terms of precision and coverage is better than the recommendation systems based on any of the individual models.