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.
Описание
Folksonomy link prediction based on a tripartite graph for tag recommendation - Online First - Springer
%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
}