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Studying the Feasibility of a Recommender in a Citizen Web Portal Based on User Modeling and Clustering Algorithms

Overview This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of the approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical algorithms, Gaussian mixtures trained by the expectation-maximization algorithm, and Kohonen's Self-Organizing Maps (SOM).

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Publisher
Reed Elsevier
File Format
PDF
Date Published
Dec 4, 2008
Format
White Papers
Topics
Portals, Software Engineering

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