Semantic bayesian profiling services for information recommendation

Authors:
Pierpaolo Basile;Eufemia Tinelli;Marco Degemmis;Tommaso Di Noia;Giovanni Semeraro;Eugenio Di Sciascio
Affiliations:
Department of Computer Science, University of Bari, Bari, Italy;Department of Computer Science, University of Bari, Bari, Italy;Department of Computer Science, University of Bari, Bari, Italy;SisInfLab, Politecnico of Bari, Bari, Italy;Department of Computer Science, University of Bari, Bari, Italy;SisInfLab, Politecnico of Bari, Bari, Italy
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

This paper presents an approach for the recommendation of items represented by different kinds of features. The motivation behind our research is that often, in online catalogues, items to be recommended are described both by textual features and by non-textual features. For example, books on Amazon.com are described by title, authors, abstract, but also by price and year of publication. Both types of features are useful to decide whether the item should be recommended to the customer. We propose an approach which integrates non-standard inference services and a Naïve Bayes profiling system able to analyze the textual features of the items by advanced natural language processes and to learn semantic user profiles exploited in the recommendation process.