Semantic Bayesian Profiling Services for Information Recommendation

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 Naive Bayes profiling system able to analyze the textual features of the items by advanced natural language processes and to learn seman-tic user profiles exploited in the recommendation process.