Date: 14 Nov 2012
Folksonomy link prediction based on a tripartite graph for tag recommendation
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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.
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- Folksonomy link prediction based on a tripartite graph for tag recommendation
Journal of Intelligent Information Systems
Volume 40, Issue 2 , pp 307-325
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Graph-based ranking
- Link prediction
- Social tagging
- Tag recommendation
- Tripartite graph
- Industry Sectors
- Author Affiliations
- 1. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, Ottawa, ON, Canada, K1N 6N5
- 2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar