Abstract
On many modern Web platforms users can annotate the available online resources with freely-chosen tags. This Social Tagging data can then be used for information organization or retrieval purposes. Tag recommenders in that context are designed to help the online user in the tagging process and suggest appropriate tags for resources with the purpose to increase the tagging quality. In recent years, different algorithms have been proposed to generate tag recommendations given the ternary relationships between users, resources, and tags. Many of these algorithms however suffer from scalability and performance problems, including the popular FolkRank algorithm. In this work, we propose a neighborhood-based tag recommendation algorithm called LocalRank, which in contrast to previous graph-based algorithms only considers a small part of the user-resource-tag graph. An analysis of the algorithm on a popular social bookmarking data set reveals that the recommendation accuracy is on a par with or slightly better than FolkRank while at the same time recommendations can be generated instantaneously using a compact in-memory representation.
This is a preview of subscription content, log in via an institution to check access.
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
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems - An Introduction. Cambridge University Press, Cambridge (2010)
Sen, S., Harper, F.M., LaPitz, A., Riedl, J.: The quest for quality tags. In: Proc. ACM GROUP 2007, Sanibel Island, Florida, USA, pp. 361–370 (2007)
Begelman, G., Keller, P., Smadja, F.: Automated tag clustering: Improving search and exploration in the tag space. In: Proc. Collaborative Web Tagging Workshop at WWW 2006, Edinburgh, Scotland (2006)
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks 30(1-7), 107–117 (1998)
Rendle, S., Balby Marinho, L., Nanopoulos, A., Lars, S.T.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proc. ACM SIGKDD 2009, Paris, France, pp. 727–736 (2009)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Trans. Knowl. Data. En. 22, 179–192 (2010)
Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proc. ACM RecSys 2009, New York, USA, pp. 61–68 (2009)
Hu, M., Lim, E.P., Jiang, J.: A probabilistic approach to personalized tag recommendation. In: Proc. IEEE SocialCom 2010, Minneapolis, MN, USA, pp. 33–40 (2010)
Bundschus, M., Yu, S., Tresp, V., Rettinger, A., Dejori, M., Kriegel, H.P.: Hierarchical bayesian models for collaborative tagging systems. In: Proc. IEEE ICDM 2009, pp. 728–733 (2009)
Gemmell, J., Schimoler, T., Mobasher, B., Burke, R.: Hybrid tag recommendation for social annotation systems. In: Proc. ACM CIKM, Toronto, pp. 829–838 (2010)
Chirita, P.A., Costache, S., Nejdl, W., Handschuh, S.: P-tag: Large scale automatic generation of personalized annotation tags for the web. In: Proc. WWW 2007, Banff, Alberta, Canada, pp. 845–854 (2007)
Song, Y., Zhuang, Z., Li, H., Zhao, Q., Li, J., Lee, W.C., Giles, C.L.: Real-time automatic tag recommendation. In: Proc. SIGIR 2008, Singapore, pp. 515–522 (2008)
Jäschke, R., Marinho, L., Hotho, A., Lars, S.T., Gerd, S.: Tag recommendations in social bookmarking systems. AI Commun. 21, 231–247 (2008)
Jäschke, R., Marinho, L.B., 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)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)
Rendle, S., Lars, S.T.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proc. ACM WSDM 2010, New York, USA, pp. 81–90 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kubatz, M., Gedikli, F., Jannach, D. (2011). LocalRank - Neighborhood-Based, Fast Computation of Tag Recommendations. In: Huemer, C., Setzer, T. (eds) E-Commerce and Web Technologies. EC-Web 2011. Lecture Notes in Business Information Processing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23014-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-23014-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23013-4
Online ISBN: 978-3-642-23014-1
eBook Packages: Computer ScienceComputer Science (R0)