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Your search keyword '"Sciascio, Eugenio Di"' showing total 35 results
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35 results on '"Sciascio, Eugenio Di"'

14. Semantic-enhanced Bluetooth discovery protocol for m-commerce applications

Author

Ruta, Michele, Noia, Tommaso Di, Sciascio, Eugenio Di, and Donini, Francesco Maria

Subjects
Bluetooth technology, Electronic commerce, Market trend/market analysis, Bluetooth technology -- Usage, Electronic commerce -- Forecasts and trends, Electronic commerce -- Analysis
Published
2006

16. SPrank

Author

Noia, Tommaso Di, primary, Ostuni, Vito Claudio, additional, Tomeo, Paolo, additional, and Sciascio, Eugenio Di, additional

Published
2016
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23. Sound and Music Recommendation with Knowledge Graphs

Author

Oramas, Sergio, Ostuni, Vito Claudio, Noia, Tommaso Di, Serra, Xavier, and Sciascio, Eugenio Di

Abstract

The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user’s download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user’s listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.

Published
2017
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