Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area.
It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm becaus...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 67; no. 7; pp. 1679 - 1703 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jul2016
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| Acceso en línea: | Ver este registro en EBSCOhost |