A Semantic Hybrid Approach for Sound Recommendation

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

Abstract: In 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.