The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Social tag prediction
Abstract
In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,789
A Comparative Study on Feature Selection in Text Categorization
Yang, Y., & Pedersen, J. (1997) · Cited by 4,790
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
Usage patterns of collaborative tagging systems
Golder, S. A., & Huberman, B. A. (2006) · Journal of Information Science · doi:10.1177/0165551506062337 · Cited by 1,817
Making Large-Scale Support Vector Machine Learning Practical
Joachims, T. (1998) · Advances in Kernel Methods · doi:10.7551/mitpress/1130.003.0015 · Cited by 1,613
A support vector method for multivariate performance measures
Joachims, T. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102399 · Cited by 786
Enhanced hypertext categorization using hyperlinks
Chakrabarti, S., Dom, B., & Indyk, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276332 · Cited by 769
Can social bookmarking improve web search?
Heymann, P., Koutrika, G., & Garcia-Molina, H. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341558 · Cited by 434
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Cited by
Pairwise interaction tensor factorization for personalized tag recommendation
Rendle, S., & Schmidt-Thieme, L. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718498 · Cited by 675
Latent dirichlet allocation for tag recommendation
Krestel, R., Fankhauser, P., & Nejdl, W. (2009) · Proceedings of the third ACM conference on Recommender systems · doi:10.1145/1639714.1639726 · Cited by 494
Evaluating similarity measures for emergent semantics of social tagging
Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., & Stumme, G. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526796 · Cited by 287
Metadata
- Authors
- Heymann, P., Ramage, D., & Garcia-Molina, H.
- Venue
- Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2008-07-20
- Pages
- 531-538
- OpenAlex
- W2090041477
- Cited by
- 311
- References
- 19
- FWCI
- 36.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
