A Semantic Hybrid Approach for Sound Recommendation

@article{Ostuni2015ASH,
  title={A Semantic Hybrid Approach for Sound Recommendation},
  author={Vito Claudio Ostuni and T. D. Noia and Eugenio Di Sciascio and Sergio Oramas and Xavier Serra},
  journal={Proceedings of the 24th International Conference on World Wide Web},
  year={2015}
}
  • V. Ostuni, T. D. Noia, X. Serra
  • Published 18 May 2015
  • Computer Science
  • Proceedings of the 24th International Conference on World Wide Web
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… 
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References

SHOWING 1-7 OF 7 REFERENCES
Extending Tagging Ontologies with Domain Specific Knowledge
TLDR
Preliminary research is described around the idea of extending tagging ontologies by incorporating some domain specific class definitions and relations to bring more meaningful structure into folksonomies and improve browsing and organisation functionalities of online platforms relying on tagging systems.
Entity Linking meets Word Sense Disambiguation: a Unified Approach
TLDR
Babelfy is presented, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations.
A Linked Data Recommender System Using a Neighborhood-Based Graph Kernel
TLDR
This paper presents a CB-RS that leverages LOD and profits from a neighborhood-based graph kernel that is able to compute semantic item similarities by matching their local neighborhood graphs.
Hybrid Recommender Systems: Survey and Experiments
  • R. Burke
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2004
TLDR
This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt.
Sparse linear methods with side information for top-n recommendations
TLDR
A set of sparse linear methods with side information (SSLIM) is proposed, which involve a regularized optimization process to learn a sparse aggregation coefficient matrix based on both user-item purchase profiles and item side information.
Evaluation of recommendations: rating-prediction and ranking
TLDR
This paper examines both rating prediction and ranking approaches in detail, and finds that the dominating difference lies instead in the training and test data considered: rating prediction is concerned with only the observed ratings, while ranking typically accounts for all items in the collection, whether the user has rated them or not.