On the Effect of Group Structures on Ranking Strategies in Folksonomies

摘要: Folksonomies have shown interesting potential for improving information discovery and exploration. Recent folksonomy systems explore the use of tag assignments, which combine Web resources with annotations (tags), users that created annotations. This article investigates on effect grouping in folksonomies, i.e. creating sets resources, using this additional structure tasks search & ranking, recommendations. We propose several group-sensitive extensions graph-based recommendation algorithms, compare them non versions. Our experiments show quality result ranking can be significantly improved by introducing exploiting (one-tailed t-Test, level significance α=0.05). Furthermore, recommendations profit from group context, it is possible to make very good even untagged resources– currently known algorithms cannot fulfill.