Abstract
Collaborative social tagging is a popular and convenient way to organize web resources. All tags compose into a semantic structure named as folksonomies. Automatic tag suggestions can ease tagging activities of users. Various methods have been proposed for tag suggestions, which are roughly categorized into two approaches: content-based and graph-based. In this paper we present a heat diffusion method, i.e., FolkDiffusion, to rank tags for tag suggestions. Compared to existing graph-based methods, FolkDiffusion can suggest user- and resource-specific tags and prevent from topic drift. Experiments on real online social tagging datasets show the efficiency and effectiveness of FolkDiffusion compared to existing graph-based methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation 15(6), 1373–1396 (2003)
Eisterlehner, F., Hotho, A., Jaschke, R.: ECML PKDD Discovery challenge 2009. In: CEUR-WS. org (2009)
Jaschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)
Jaschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems. AI Communications 21(4), 231–247 (2008)
Katakis, I., Tsoumakas, G., Vlahavas, I.: Multilabel text classification for automated tag suggestion. In: ECML/PKDD Discovery Challenge 2008, p. 75 (2008)
Lafferty, J., Lebanon, G.: Diffusion kernels on statistical manifolds. The Journal of Machine Learning Research 6, 163 (2005)
Lipczak, M.: Tag recommendation for folksonomies oriented towards individual users. In: ECML/PKDD Discovery Challenge 2008, p. 84 (2008)
Ma, H., Yang, H., King, I., Lyu, M.R.: Learning latent semantic relations from clickthrough data for query suggestion. In: Proceeding of CIKM, pp. 709–718 (2008)
Ma, H., Yang, H., Lyu, M.R., King, I.: Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceeding of CIKM, pp. 233–242 (2008)
Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: Proceedings of WWW, pp. 953–954 (2006)
Ohkura, T., Kiyota, Y., Nakagawa, H.: Browsing system for weblog articles based on automated folksonomy. In: Proceedings of WWW (2006)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Tech. rep, Stanford University (1998)
Rendle, S., Schmidt-Thieme, L.: Factor models for tag recommendation in bibsonomy. In: ECML/PKDD Discovery Challenge (2009)
Sood, S., Owsley, S., Hammond, K., Birnbaum, L.: TagAssist: Automatic tag suggestion for blog posts. In: Proceedings of ICWSM, p. 28 (2007)
Tatu, M., Srikanth, M., D’Silva, T.: RSDC 2008: Tag recommendations using bookmark content. In: ECML/PKDD Discovery Challenge 2008(2008)
Xu, Z., Fu, Y., Mao, J., Su, D.: Towards the semantic web: Collaborative tag suggestions. In: Collaborative Web Tagging Workshop at WWW 2006 (2006)
Yang, H., King, I., Lyu, M.R.: DiffusionRank: a possible penicillin for web spamming. In: Proceedings of SIGIR, pp. 431–438 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Z., Shi, C., Sun, M. (2010). FolkDiffusion: A Graph-Based Tag Suggestion Method for Folksonomies. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-17187-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17186-4
Online ISBN: 978-3-642-17187-1
eBook Packages: Computer ScienceComputer Science (R0)