Improving Similarity Measures Using Ontological Data
| Title | Improving Similarity Measures Using Ontological Data |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Sürer, Özge |
| Conference Name | Proceedings of the Eleventh ACM Conference on Recommender Systems |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4652-8 |
| Keywords | compositionality, metadata, Metadata Discovery Problem, Ontology, pubcrawl, recommender systems, Resiliency, Scalability |
| Abstract | The representation of structural data is important to capture the pattern between features. Interrelations between variables provide information beyond the standard variables. In this study, we show how ontology information may be used in a recommender systems to increase the efficiency of predictions. We propose two alternative similarity measures that incorporates the structural data representation. Experiments show that our ontology-based approach delivers improved classification accuracy when the dimension increases. |
| URL | http://doi.acm.org/10.1145/3109859.3109863 |
| DOI | 10.1145/3109859.3109863 |
| Citation Key | surer_improving_2017 |