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The information contained in social tagging systems is often modelled as agraph of connections between users, items and tags. Recommendation algorithmssuch as FolkRank, have the potential to leverage complex relationships in thedata, corresponding to multiple hops in the graph. We present an in-depthanalysis and evaluation of graph models for social tagging data and proposenovel adaptations and extensions of FolkRank to improve tag recommendations. Wehighlight implicit assumptions made by the widely used folksonomy model, andpropose an alternative and more accurate graph-representation of the data. Ourextensions of FolkRank address the new item problem by incorporating contentdata into the algorithm, and significantly improve prediction results onunpruned datasets. Our adaptations address issues in the iterative weightspreading calculation that potentially hinder FolkRank's ability to leveragethe deep graph as an information source. Moreover, we evaluate the benefit ofconsidering each deeper level of the graph, and present important insightsregarding the characteristics of social tagging data in general. Our resultssuggest that the base assumption made by conventional weight propagationmethods, that closeness in the graph always implies a positive relationship,does not hold for the social tagging domain.

Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations