Folksonomy link prediction based on a tripartite graph for tag recommendation
Author | Rawashdeh, Majdi |
Author | Kim, Heung-Nam |
Author | Alja'am, Jihad Mohamad |
Author | El Saddik, Abdulmotaleb |
Available date | 2024-03-20T01:55:07Z |
Publication Date | 2013 |
Publication Name | Journal of Intelligent Information Systems |
Resource | Scopus |
ISSN | 9259902 |
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. |
Sponsor | Acknowledgement This publication was made possible by a grant from the Qatar National Research Fund NPRP 09-052-5-003. |
Language | en |
Publisher | Springer |
Subject | Folksonomy Graph-based ranking Link prediction Social tagging Tag recommendation Tripartite graph |
Type | Article |
Pagination | 307-325 |
Issue Number | 2 |
Volume Number | 40 |
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Computer Science & Engineering [2284 items ]