Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms

Authors: Martín-Guerrero, J.D., Palomares, A., Balaguer-Ballester, E., Soria-Olivas, E., Gómez-Sanchis, J. and Soriano-Asensi, A.

Journal: Expert Systems with Applications

Volume: 30

Issue: 2

Pages: 299-312

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2005.07.025

Abstract:

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 our 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). The most accurate clustering is yielded by SOM. Afterwards, we turn to real data. The users of a citizen web portal (Infoville XXI, http://www.infoville.es) are clustered. The clustering achieved enables us to study the future success of a collaborative recommender by means of a prediction strategy. New users are recommended according to the cluster in which they have been classified. The suitability of the recommendation is evaluated by checking whether or not the recommended objects correspond to those actually selected by the user. The results show the relevance of the information provided by clustering algorithms in this web portal, and therefore, the relevance of developing a collaborative recommender for this web site. © 2005 Elsevier Ltd. All rights reserved.

Source: Scopus