Skip to main content
SpringerLink
Log in
Book cover

The Influence of Technology on Social Network Analysis and Mining pp 497–519Cite as

An Adaptive Framework for Discovery and Mining of User Profiles from Social Web-Based Interest Communities

  • Chapter
  • First Online:

  • 2596 Accesses

Part of the book series: Lecture Notes in Social Networks ((LNSN,volume 6))

Abstract

Within this paper we introduce an adaptive framework for semi- to fully-automatic discovery, acquisition and mining of topic style interest profiles from openly accessible social web communities. To do such, we build an adaptive taxonomy search tree from target domain (domain towards which we are gathering and processing profiles for), starting with generic concepts at root moving down to specific-level instances at leaves, then we utilize one of proposed Quest schemes to read the concept labels from the tree and crawl the source social network repositories for profiles containing matching and related topics. Using machine learning techniques, cached profiles are then mined in two consecutive steps, utilizing a clusterer and a classifier in order to assign and predict correct profiles to their corresponding clustered corpus, which are retrieved later on by an ontology-based recommender to suggest and recommend the community members with the items of their similar interest. Focusing on increasingly important digital cultural heritage context, using a set of profiles acquired from an openly accessible social network, we test the accuracy and adaptivity of framework. We will show that a tradeoff between schemes proposed can lead to adaptive discovery of highly relevant profiles.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    LiveJournal interests. http://www.livejournal.com/interests.bml

  2. 2.

    Museum of History of Science at Florence, Italy. http://www.museogalileo.it/en/index.html

  3. 3.

    Museum of Fine Arts, Malta. http://www.heritagemalta.org/museums/finearts/fineartsinfo.html

References

  1. LiveJournal (2010). http://www.livejournal.com/, last accessed 2010

  2. Ghosh, R., Dekhil, M.: Discovering user profiles. In: Proceedings of WWW 2009, pp. 1233–1234. ACM, New York (2009)

    Google Scholar 

  3. Teevan, J., Dumais, S., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 456. ACM, New York (2005)

    Google Scholar 

  4. Liu, H., Maes, P., Davenport, G.: Unraveling the taste fabric of social networks, Int. J. Semant. Web Inf. Syst. 2, 42–71 (2006)

    Article  Google Scholar 

  5. Pretschner, A., Gauch, S.: Ontology based personalized search. In: Proceedings, 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 391–398 (1999). http://dx.doi.org/10.1109/TAI.1999.809829

  6. Trajkova, J., Gauch, S.: Improving ontology-based user profiles. Proc. RIAO 4, 380–389 (2004)

    Google Scholar 

  7. Dokoohaki, N., Matskin, M.: Personalizing human interaction through hybrid ontological profiling: cultural heritage case study (2008). http://eprints.ecs.soton.ac.uk/15451/

  8. Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching ontological user profiles with tagging history for multi-domain recommendations. In: 1st International Workshop on Collective Semantics: Collective Intelligence & the Semantic Web (CISWeb 2008). CEUR-WS (2008). http://ceur-ws.org/Vol-351

  9. Razmerita, L., Angehrn, A., Maedche, A.: Ontology-Based User Modeling for Knowledge Management Systems. Lecture Notes in Computer Science, pp. 213–217. Springer, Berlin/New York (2003)

    Google Scholar 

  10. Felden, C., Linden, M.: Ontology-based user profiling. Business Information Systems, 314–327. doi:10.1007/978-3-540-72035-5_24

    Google Scholar 

  11. Sieg, A., Mobasher, B., Burke, R.: Ontological user profiles for representing context in web search. In: Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops, pp. 91–94. IEEE, Los Alamitos (2007)

    Google Scholar 

  12. Szomszor, M., Alani, H., Cantador, I., O’Hara, K., Shadbolt, N.: Semantic modelling of user interests based on cross-folksonomy analysis. In: International Semantic Web Conference, pp. 632–648. Springer, Berlin/New York (2008)

    Google Scholar 

  13. Gauch, S., Chaffee, J., Pretschner, A.: Ontology-based user profiles for search and browsing. Web Intell. Agent Syst. 1, 219–234 (2003)

    Google Scholar 

  14. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002). doi:10.1145/505282.505283

    Article  Google Scholar 

  15. Cooley, R., Mobasher, B., Srivastava, J.: Web mining: information and pattern discovery on the world wide web. In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-97), vol. 1, pp. 558–567. IEEE, Los Alamitos (1997)

    Google Scholar 

  16. Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization, ACM Trans. Internet Technol. 3, 1–27 (2003)

    Article  Google Scholar 

  17. Mobasher, B.: Data mining for web personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, pp. 90–135. Springer, Berlin/Heidelberg (2007). doi:10.1007/978-3-540-72079-9_3

    Google Scholar 

  18. Mobasher, B.: A web personalization engine based on user transaction clustering. In: Proceedings of the 9th Workshop on Information Technologies and Systems (WITS’99), Charlotte (1999)

    Google Scholar 

  19. O’Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: The Proceedings of SIGIR-2001 Workshop on Recommender Systems, New Orleans. ACM, New York (2001)

    Google Scholar 

  20. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns from web data, ACM SIGKDD Explor. Newsl. 1, 23 (2000)

    Article  Google Scholar 

  21. Middleton, S.E., Shadbolt, N.R. De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22, 54–88 (2004)

    Article  Google Scholar 

  22. Pazzani, M., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, pp. 325–341. Springer, Berlin/Heidelberg (2007)

    Google Scholar 

  23. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering (1999). http://portal.acm.org/citation.cfm?id=312682

  24. Soltysiak, S.J., Crabtree, I.B.: Automatic learning of user profiles: towards the personalisation of agent services. BT Technol. J. 16, 110–117 (1998)

    Article  Google Scholar 

  25. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27, 313–331 (1997)

    Article  Google Scholar 

  26. Wulf, V., Reichling, T.: Expert recommender systems in practice. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems CHI 09, p. 59. ACM, New York (2009)

    Google Scholar 

  27. Wallach, H.M.: Topic modeling: beyond bag-of-words, Language 977–984 (2006)

    Google Scholar 

  28. Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., Rai, A., Madan, S.: User interests in social media sites: an exploration with micro-blogs. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 1823–1826. ACM, New York (2009)

    Google Scholar 

  29. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)

    Article  MathSciNet  Google Scholar 

  30. Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems RecSys 09, vol. 61. ACM, New York (2009)

    Google Scholar 

  31. Jensen, D., Neville, J.: Data mining in social networks. In: National Academy of Sciences Workshop on Dynamic Social Network Modeling and Analysis. National Academies Press (2002)

    Google Scholar 

  32. Liu, H., Maes, P.: Interestmap: Harvesting social network profiles for recommendations. In: Beyond Personalisation (2004). http://web.media.mit.edu/~hugo/publications/papers/BP2005-hugo-interestmap.pdf

  33. Dokoohaki, N., Matskin, M.: Quest: an adaptive framework for user profile acquisition from social communities of interest. In: International Conference on Advances in Social Network Analysis and Mining, pp. 360–364. IEEE, Los Alamitos (2010)

    Google Scholar 

  34. Novak, B.: A survey of focused web crawling algorithms. In: Proceedings of SIKDD, pp. 55–58. ACM, New York (2004)

    Google Scholar 

  35. Ester, M., Grob, M., Kriegel, H.P.: Focused web crawling: a generic framework for specifying the user interest and for adaptive crawling strategies. In: Proceedings of 27th International Conference on Very Large Data Bases, pp. 321–329. Morgan Kaufmann, Orlando (2001)

    Google Scholar 

  36. Chakrabarti, S., Van Den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific Web resource discovery. Comput. Netw. 31, 1623–1640 (1999)

    Article  Google Scholar 

  37. Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)

    MATH  Google Scholar 

  38. Baharudin, B., Lee, L.H., Khan, K.: A review of machine learning algorithms for text-documents classification. J. Adv. Inf. Technol. 1, 4–20 (2010)

    Google Scholar 

  39. Ruotsalo, T.: Methods and Applications for Ontology-Based Recommender Systems (2010). lib.tkk.fi

  40. Krestel, R., Fankhauser, P.: Tag recommendation using probabilistic topic models. In: Eisterlehner, F., Hotho, A., Jäschke, R. (eds.) ECML PKDD Discovery Challenge 2009 (DC09), vol. 497, p. 131. CEUR-WS (2009). http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/

  41. Ruotsalo, T., Mäkelä, E., Kauppinen, T., Hyvönen, E., Haav, K., Rantala, V., Frosterus, M., Dokoohaki, N., Matskin, M.: Smartmuseum: personalized context-aware access to digital cultural heritage (2009). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.164.9014

  42. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  43. Marwick, A.: LiveJournal Users, New York (2008)

    Google Scholar 

  44. Porter, M.: The porter stemming algorithm (2001). http://tartarus.org/~martin/PorterStemmer/

  45. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24, 881–892 (2002)

    Article  Google Scholar 

  46. John, G., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  47. Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)

    Google Scholar 

  48. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  49. Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 06, vol. 54, p. 178. ACM, New York (2006)

    Google Scholar 

  50. Krestel, R., Chen, L.: The art of tagging: measuring the quality of tags. In: Domingue, J., Anutariya, C. (eds.) Proceedings of the 3rd Asian Semantic Web Conference, pp. 257–271. Springer, Berlin/Heidelberg (2008)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software and Computer Systems (SCS), School of Information and Telecommunication Technology (ICT), Royal Institute of Technology (KTH), Stockholm, Sweden

    Nima Dokoohaki

  2. Computer and Information Science (IDI), Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Mihhail Matskin

Authors
  1. Nima Dokoohaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mihhail Matskin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nima Dokoohaki .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, TOBB University, Sogutozu Cad No. 43, Sogutozu Ankara, Turkey

    Tansel Özyer

  2. Computer Science, University of Calgary, University Dr. NW 2500, Calgary, T2N 1N4, Canada

    Jon Rokne

  3. IPSC, European Commission Joint Research Cent., Via Enrico Fermi 2749, Ispra, 21027, Italy

    Gerhard Wagner

  4. De Wetstraat 16, Leiden, 2332 XT, Netherlands

    Arno H.P. Reuser

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Wien

About this chapter

Cite this chapter

Dokoohaki, N., Matskin, M. (2013). An Adaptive Framework for Discovery and Mining of User Profiles from Social Web-Based Interest Communities. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds) The Influence of Technology on Social Network Analysis and Mining. Lecture Notes in Social Networks, vol 6. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1346-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-1346-2_22

  • Published:

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1345-5

  • Online ISBN: 978-3-7091-1346-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation