33 research outputs found
Descrição morfológica dos ramos colaterais viscerais da aorta abdominal do macaco-de-cheiro
Ruminal parameters of bovines fed diets based on sugar cane with doses of calcium hydroxide
Macrofauna edáfica em estádios sucessionais de Floresta Estacional Semidecidual e pastagem mista em Pinheiral (RJ): Rio de Janeiro State
Description of the soil and root biomass of two subtropical mangroves in Antonina and Guaratuba Bay, Paraná State, Brazil
Anatomia do cone medular aplicada à via epidural de administração de fármacos em macacos-prego ( Sapajus libidinosus )
Relação peso-comprimento e fator de condição de Oligosarcus hepsetus (Cuvier, 1829) no Parque Estadual da Serra do Mar - Núcleo Santa Virgínia, Mata Atlântica, estado de São Paulo, Brasil
Fenologia, produção e composição do mosto da 'Cabernet sauvignon' e 'Tannat' em clima subtropical
Muscularity and adiposity of carcass of Santa Inês lambs: effects of different levels of replacement of ground corn by forage cactus meal in finishing ration
Improving Tag Recommendation Using Few Associations
Collaborative tagging services allow users to freely assign tags to resources. As the large majority of users enters only very few tags, good tag recommendation can vastly improve the usability of tags for techniques such as searching, indexing, and clustering. Previous research has shown that accurate recommendation can be achieved by using conditional probabilities computed from tag associations. The main problem, however, is that enormous amounts of associations are needed for optimal recommendation.
We argue and demonstrate that pattern selection techniques can improve tag recommendation by giving a very favourable balance between accuracy and computational demand. That is, few associations are chosen to act as information source for recommendation, providing high-quality recommendation and good scalability at the same time.
We provide a proof-of-concept using an off-the-shelf pattern selection method based on the Minimum Description Length principle. Experiments on data from Delicious, LastFM and YouTube show that our proposed methodology works well: applying pattern selection gives a very favourable trade-off between runtime and recommendation quality.status: publishe