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
Semantic Bayesian profiling services for information recommendation / Basile, P; Tinelli, E; Degemmis, M; DI NOIA, Tommaso; Semeraro, G; DI SCIASCIO, Eugenio. - 4694:PART 3(2007), pp. 711-719. (Intervento presentato al convegno 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007 tenutosi a Vietri sul Mare nel September 12-14, 2007) [10.1007/978-3-540-74829-8_87].
Semantic Bayesian profiling services for information recommendation
DI NOIA, Tommaso;DI SCIASCIO, Eugenio
2007-01-01
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 processI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
