2 research outputs found
Measuring vertex centrality in co-occurrence graphs for online social tag recommendation
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative
bookmarking systems. This model receives as input a bookmark of a web page
or scientific publication, and automatically suggests a set of social tags useful
for annotating the bookmarked document. Analysing and processing the
bookmark textual contents - document title, URL, abstract and descriptions - we
extract a set of keywords, forming a query that is launched against an index,
and retrieves a number of similar tagged bookmarks. Afterwards, we take the
social tags of these bookmarks, and build their global co-occurrence sub-graph.
The tags (vertices) of this reduced graph that have the highest vertex centrality
constitute our recommendations, whThis research was supported by the European Commission under
contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA.
The expressed content is the view of the authors but not necessarily the view of
SALERO, MIAUCE and SEMEDIA projects as a whol