Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
LocalRank – A graph-based tag recommender
verfasst von : Dr. Fatih Gedikli
Erschienen in: Recommender Systems and the Social Web
Verlag: Springer Fachmedien Wiesbaden
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Tag recommenders are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality [Jäschke et al., 2008]. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags, see, for example, [Rendle et al., 2009; Rendle and Schmidt-Thie, 2010] or [Gemmell et al., 2010]. Many of these algorithms, however, suffer from scalability and performance problems, including the popular
FolkRank
algorithm [Hotho et al., 2006]. For example, even when using only a small excerpt of a commonly used social bookmarking data set, FolkRank requires about 20 seconds on a typical desktop PC (AMD Athlon II Dual Core, 2.9Ghz, 8GB Ram) to compute a single recommendation list.