Government-to-Business personalized e-services using semantic-enhanced recommender system

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 6866 LNCS pp. 197 - 211
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The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking 'one-to-one' e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semantic-enhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems. © 2011 Springer-Verlag Berlin Heidelberg.
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