WebPUM: A Web-based recommendation system to predict user future movements

Abstract

Web usage mining has become the subject of exhaustive research, as its potential for Webbased personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users' future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy.

A Classification Algorithm to Improve the Design of Websites

“…M. Jalali, N. Mustapha et al developed a Web-based recommendation system known as Web-ORS for online prediction through Web usage mining system. They also proposed a novel approach for classifying user navigation patterns to predict user future intentions [1,2].…”

“…Coverage measures the ability of the prediction engine to produce all of the page views that are likely to be visited by the user [2].…”

“…Accuracy measure is defined as degree to which the prediction engine produces accurate recommendations [2]. Original np : Original Navigational pattern of user.…”

“…The analyzed results from Web navigation behaviors are indispensable knowledge to intelligent online applications and Web based personalization system in improving searching accuracy during information seeking. in traditional personalization techniques [2].…”

Semantically Enriched Web Usage Mining for Predicting User Future Movements

“…In this work, we propose to extend the WebPUM approach described in [5] with rich semantic data characterizing the contents of the Web pages and Web site structure characterizing the topology of the Web site. More precisely, we propose a Semantically enriched Web Usage Mining method (SWUM) and argue that by incorporating semantic and site structure data into WebPUM we will be able to improve the recommendation accuracy.…”