An approach based on the Adaptive Resonance Theory for analysing the viability of recommender systems in a citizen Web portal

This paper proposes a methodology to optimise the future accuracy of a collaborative recommender application in a citizen Web portal. There are four stages namely, user modelling, benchmarking of clustering algorithms, prediction analysis and recommendation. The first stage is to develop analytical models of common characteristics of Web-user data. These artificial data sets are then used to evaluate the performance of clustering algorithms, in particular benchmarking the ART2 neural network with K-means clustering. Afterwards, it is evaluated the predictive accuracy of the clusters applied to a real-world data set derived from access logs to the citizen Web portal (). The results favour ART2 algorithms for cluster-based collaborative filtering on this Web portal. Finally, a recommender based on ART2 is developed. The follow-up of real recommendations will allow to improve recommendations by including new behaviours that are observed when users interact with the recommender system.