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A Formal Framework for Hypergraph-Based User Profiles

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Information Sciences and Systems 2014

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Abstract

In this study, we propose a formal framework for user profile representation with hypergraphs. We exploit the framework to aggregate partial profiles of the individual to obtain a complete, multi-domain user model, since we aim to model the user from several perspectives. We use Freebase commons package concepts as predefined domains. The proposed user model is also capable of extracting user domain capsules, which models the user for the domain of interest. Moreover, using a hypergraph data structure results in solving connection-based problems easily, since the cost of local operations on a graph is low and independent of the size of the whole graph. Many problems in user modelling domain are connection-based problems, such as recommendation.

This work is partially supported by The Scientific and Technical Council of Turkey Grant “TUBITAK EEEAG-112E111”.

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Notes

  1. 1.

    Freebase, https://www.freebase.com/.

References

  1. F. Abel, E. Herder, G.J. Houben, N. Henze, D. Krause, Cross-system user modeling and personalization on the social web. User Model. User-Adapt. Interact. 23(2–3), 169–209 (2013)

    Article  Google Scholar 

  2. A. Tiroshi, S. Berkovsky, M.A. Kaafar, T. Chen, T. Kuflik, Cross social networks interests predictions based ongraph features, in Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13) (ACM, New York, 2013), pp. 319–322

    Google Scholar 

  3. B. Chen, J. Wang, Q. Huang, T. Mei, Personalized video recommendation through tripartite graph propagation. in Proceedings of the 20th ACM International Conference on Multimedia (ACM, 2012 ), pp. 1133–1136

    Google Scholar 

  4. L. Li, T. Li, News recommendation via hypergraph learning: encapsulation of user behavior and news content. in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (ACM, 2013), pp. 305–314

    Google Scholar 

  5. T. Kramr, M. Barla, M. Bielikov, Personalizing search using socially enhanced interest model, built from the stream of user’s activity. J. Web Eng. 12(1–2), 65–92 (2013)

    Google Scholar 

  6. H. Tarakci, N.K. Cicekli, Ubiquitous fuzzy user modeling for multi-application environments by mining socially enhanced online traces. in User Modeling, Adaptation, and Personalization (Springer, Berlin Heidelberg, 2012), pp. 387–390

    Google Scholar 

  7. H. Taraki, N.K. Cicekli, UCASFUM: A Ubiquitous context-aware semantic fuzzy user modeling system. In KEOD (2012), pp. 278–283

    Google Scholar 

  8. F. Orlandi, J. Breslin, A. Passant, Aggregated, interoperable and multi-domain user profiles for the social web. in Proceedings of the 8th International Conference on Semantic Systems (ACM, 2012), pp. 41–48

    Google Scholar 

  9. G. Gallo, G. Longo, S. Pallottino, S. Nguyen, Directed hypergraphs and applications. Discret. Appl. Math. 42(2), 177–201 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  10. I. Robinson, J. Webber, E. Eifrem, Graph Databases (O’Reilly Media Inc., Sebastopol, 2013)

    Google Scholar 

  11. S.A. Takale, S. Nandgaonkar, Measuring semantic similarity between words using web documents. Int. J. Adv. Comput. Sci. Appl. IJACSA 1(4), 78–85 (2010)

    Google Scholar 

  12. L. Zhiqiang, S. Werimin, Y. Zhenhua, Measuring semantic similarity between words using wikipedia. in Web Information Systems and Mining, 2009. WISM 2009. International Conference on (IEEE, 2009), pp. 251–255

    Google Scholar 

  13. P. Ilakiya, M. Sumathi, S. Karthik, A survey on semantic similarity between words in semantic web. in Radar, Communication and Computing (ICRCC), 2012 International Conference on (IEEE, 2012), pp. 213–216

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sakarya University, Sakarya, Turkey

    Hilal Tarakci

  2. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Nihan Kesim Cicekli

Authors
  1. Hilal Tarakci
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  2. Nihan Kesim Cicekli
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Corresponding author

Correspondence to Hilal Tarakci .

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, Gliwice, Poland

    Tadeusz Czachórski

  2. Imperial College London, London, United Kingdom

    Erol Gelenbe

  3. Imperial College London, London, United Kingdom

    Ricardo Lent

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© 2014 Springer International Publishing Switzerland

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Tarakci, H., Cicekli, N.K. (2014). A Formal Framework for Hypergraph-Based User Profiles. In: Czachórski, T., Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-09465-6_30

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  • DOI: https://doi.org/10.1007/978-3-319-09465-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09464-9

  • Online ISBN: 978-3-319-09465-6

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