Abstract
Existing works in user profiling suffers from two well known problems in IR: polysemy and synonymy. Enriching semantics to terms that represent user interests disambiguate it’s context, polysemous topics, and synonyms. One way of enriching semantics to terms is by grouping related terms together into clusters. This work exploits users’ tweets to build a Contextualized User Interest Profile(CUIP) that consist of clusters of (semantically) related terms and their term-weights. We propose two approaches to build the CUIP: svdCUIP based on Singular Value Decomposition (SVD); and, modsvdCUIP based on modded SVD (modSVD). Experimental results show that the clustering tendency and accuracy of the modsvdCUIP cluster structure is far more superior than the svdCUIP cluster structure.
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
Noll, M.G., Meinel, C.: Web Search Personalization Via Social Bookmarking and Tagging. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990)
Kumar, H., Kim, H.G.: Using Folksonomies for Building User Interest Profile. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 438–441. Springer, Heidelberg (2011)
Shepitsen, A., Gemmell, J., Mohasher, B., Buke, R.: Personalization in Folksonomies Based on Tag Clustering. In: AAAI 2008, pp. 37–48 (2008)
Simpson, E., Butler, M.H.: Analyizing Communal Tag Relationships for Enhanced Navigation and User Modeling, pp. 43–64. IGI Global (2009)
Kaufman, L., Rousseeuw, P.J.: Introduction, in Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, Inc., Hoboken (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumar, H., Kim, HG. (2012). Semantically Enriched User Interest Profile Built from Users’ Tweets. In: Chen, HH., Chowdhury, G. (eds) The Outreach of Digital Libraries: A Globalized Resource Network. ICADL 2012. Lecture Notes in Computer Science, vol 7634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34752-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-34752-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34751-1
Online ISBN: 978-3-642-34752-8
eBook Packages: Computer ScienceComputer Science (R0)