A Hybrid Web Personalization Model Based on Site Connectivity

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.

[1]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[2]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[3]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[4]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[5]  Michael D. Smith,et al.  Using Path Profiles to Predict HTTP Requests , 1998, Comput. Networks.

[6]  Peter Pirolli,et al.  Mining Longest Repeating Subsequences to Predict World Wide Web Surfing , 1999, USENIX Symposium on Internet Technologies and Systems.

[7]  Myra Spiliopoulou,et al.  WUM: A tool for Web Utilization analysis , 1999 .

[8]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[9]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[10]  Kristian J. Hammond,et al.  Mining navigation history for recommendation , 2000, IUI '00.

[11]  Myra Spiliopoulou,et al.  The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis , 2002, WEBKDD.

[12]  Bamshad Mobasher,et al.  Impact of Site Characteristics on Recommendation Models Based On Association Rules and Sequential Patterns , 2003 .

[13]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

[14]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.