%0 Journal Article %1 rawashdeh2012folksonomy %A Rawashdeh, Majdi %A Kim, Heung-Nam %A Alja’am, JihadMohamad %A Saddik, Abdulmotaleb %D 2012 %I Springer US %J Journal of Intelligent Information Systems %K folksonomy link prediction recommendation recommender tag tripartite %P 1-19 %R 10.1007/s10844-012-0227-2 %T Folksonomy link prediction based on a tripartite graph for tag recommendation %U http://dx.doi.org/10.1007/s10844-012-0227-2 %X Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.

@article{rawashdeh2012folksonomy, abstract = {Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.}, added-at = {2013-02-19T13:45:18.000+0100}, author = {Rawashdeh, Majdi and Kim, Heung-Nam and Alja’am, JihadMohamad and Saddik, Abdulmotaleb}, biburl = {https://www.bibsonomy.org/bibtex/2d03a9bc135d4969c7b554f2290f9b902/folke}, description = {Folksonomy link prediction based on a tripartite graph for tag recommendation - Online First - Springer}, doi = {10.1007/s10844-012-0227-2}, interhash = {d9e9af76ab6fdc8fc9ce8ec5bb0b1041}, intrahash = {d03a9bc135d4969c7b554f2290f9b902}, issn = {0925-9902}, journal = {Journal of Intelligent Information Systems}, keywords = {folksonomy link prediction recommendation recommender tag tripartite}, language = {English}, pages = {1-19}, publisher = {Springer US}, timestamp = {2013-02-19T13:45:18.000+0100}, title = {Folksonomy link prediction based on a tripartite graph for tag recommendation}, url = {http://dx.doi.org/10.1007/s10844-012-0227-2}, year = 2012 }