A Semantic Hybrid Approach for Sound Recommendation

24th World Wide Web Conference - 2015
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n this work we describe a hybrid recommendation approach for recommending sounds to users by exploiting and semantically enriching textual information such as tags and sounds descriptions. As a case study we used Freesound, a popular site for sharing sound samples which counts more than 4 million registered users. Tags and textual sound descriptions are exploited to extract and link entities to external ontologies such as WordNet and DBpedia. The enriched data are eventually merged with a domain specific tagging ontology to form a knowledge graph. Based on this latter, recommendations are then computed using a semantic version of the feature combination hybrid approach. An evaluation on historical data shows improvements with respect to state of the art collaborative algorithms.

BibTex references


@InProceedings{OODSD15,
author = {Vito Claudio Ostuni and Sergio Oramas and Tommaso {Di Noia} and Xavier Serra and Eugenio {Di Sciascio}},
title = "A Semantic Hybrid Approach for Sound
Recommendation",
booktitle = "24th World Wide Web Conference",
year = "2015",
publisher = "ACM",
url = "http://sisinflab.poliba.it/publications/2015/OODSD
15"
}

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