2017 | OriginalPaper | Buchkapitel
Tuning Personalized PageRank for Semantics-Aware Recommendations Based on Linked Open Data
verfasst von : Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops
Erschienen in: The Semantic Web
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Abstract
DBpedia
node; next, the resources gathered from DBpedia
that describe the item are connected to the item nodes, thus enriching the original representation and giving rise to a tripartite data model. Such a data model can be exploited to provide users with recommendations by running PPR against the resulting representation and by suggesting the items with the highest PageRank score.DBpedia
and PPR can overcome the performance of several state-of-the-art approaches. Moreover, a proper tuning of PPR parameters, obtained by better distributing the weights among the nodes modeled in the graph, further improved the overall accuracy of the framework and confirmed the effectiveness of our strategy.