Skip to main content

A semantic social network-based expert recommender system

Abstract

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. 1.

    http://www.kdd.org/kdd2010.

  2. 2.

    http://www.informatik.uni-trier.de/ley/db/.

  3. 3.

    http://www.cs.waikato.ac.nz/ml/weka/.

  4. 4.

    http://www.casos.cs.cmu.edu/projects/ora/index.html.

References

  1. 1.

    Adamic LA (1999) The small world web. In: Proceedings of the third European conference on research and advanced technology for digital libraries, ECDL ’99, London, UK, pp 443–452

    Chapter  Google Scholar 

  2. 2.

    Kalles D, Papagelis A, Zaroliagis C (2003) Algorithmic aspects of web intelligent systems. In: Zhong N, Liu J, Yao Y (eds) Web intelligence, vol 15. Springer, Berlin, pp 323–344

    Chapter  Google Scholar 

  3. 3.

    Herlocker J, Konstan J, Riedl J (2000) Explaining collaborative filtering recommendations. In: Proceedings of CSCW, pp 241–250

    Chapter  Google Scholar 

  4. 4.

    Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst 22:89–115

    Article  Google Scholar 

  5. 5.

    Basu C, Hirsh H, Cohen WW (1998) Recommendation as classification: using social and Content-based information in recommendation. AAAI/IAAI, Menlo Park, pp 714–720

    Google Scholar 

  6. 6.

    Garden M, Dudek G (2006) Mixed collaborative and Content-based filtering with User-contributed semantic features. AAAI, Menlo Park

    Google Scholar 

  7. 7.

    Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, SIGIR ’09, New York, USA, pp 195–202

    Chapter  Google Scholar 

  8. 8.

    Good N, Schafer JB, Konstan JA, Borchers A, Sarwar B, Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the sixteenth national conference on artificial intelligence and the eleventh innovative applications of artificial intelligence conference innovative applications of artificial intelligence, AAAI ’99/IAAI ’99, Menlo Park, CA, USA, pp 439–446

    Google Scholar 

  9. 9.

    Renda ME, Straccia U (2002) A personalized collaborative digital library environment. In: Proceedings of the 5th international conference on Asian digital libraries: digital libraries: people, knowledge, and technology, ICADL ’02, London, UK, pp 262–274

    Chapter  Google Scholar 

  10. 10.

    Perugini S, Goncalves MA, Fox EA (2004) A connection centric survey of recommender system research. J Intell Inf Syst 23(1)

  11. 11.

    Lueg C (1997) Social filtering and social reality. In: Proceedings of the 5th DELOS workshop on filtering and collaborative filtering. ERCIM Press, Biot, pp 10–12

    Google Scholar 

  12. 12.

    Bedi P, Kaur H, Marwaha S (2007) Trust based recommender system for the semantic web. In: Proceedings of the 20th international joint conference on artificial intelligence, San Francisco, CA, USA, pp 2677–2682

    Google Scholar 

  13. 13.

    Yoo I, Hu X, Song I-Y (2006) Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’06, New York, USA, pp 791–796

    Chapter  Google Scholar 

  14. 14.

    Zhang X, Jing L, Hu X, Ng M, Zhou X (2007) A comparative study of ontology based term similarity measures on pubmed document clustering. In: Proceedings of the 12th international conference on database systems for advanced applications, DASFAA’07, Berlin, Heidelberg, pp 115–126

    Chapter  Google Scholar 

  15. 15.

    Baeza-Yates RA, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley Longman, Boston

    Google Scholar 

  16. 16.

    Hotho A, Maedche A, Staab S (2001) Text clustering based on good aggregations. In: Proceedings of the 2001 IEEE international conference on data mining, ICDM ’01, Washington, DC, USA, pp 607–608

    Chapter  Google Scholar 

  17. 17.

    Hotho A, Staab S, Stumme G (2003) WordNet improves text document clustering. In: Ding Y, van Rijsbergen K, Ounis I, Jose J (eds) Proceedings of the semantic web workshop of the 26th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR 2003), Toronto, Canada

    Google Scholar 

  18. 18.

    Sinha RR, Swearingen K (2001) Comparing recommendations made by online systems and friends. In: DELOS workshop: personalisation and recommender systems in digital libraries

    Google Scholar 

  19. 19.

    Wang P, Hu J, Zeng H-J, Chen L, Chen Z (2007) Improving text classification by using encyclopedia knowledge. In: Proceedings of the 2007 seventh IEEE international conference on data mining, Washington, DC, USA, pp 332–341

    Google Scholar 

  20. 20.

    Eyharabide V, Amandi A (2012) Ontology-based user profile learning. Appl Intell 36(4):857–869

    Article  Google Scholar 

  21. 21.

    Lee LH, Wan CH, Rajkumar R, Isa D (2012) An enhanced support vector machine classification framework by using Euclidean distance function for text document categorization. Appl Intell 37(1):80–99

    Article  Google Scholar 

  22. 22.

    Daud A, Muhammad F (2012) Group topic modeling for academic knowledge discovery. Appl Intell 36(4):870–886

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Keivan Kianmehr.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Davoodi, E., Kianmehr, K. & Afsharchi, M. A semantic social network-based expert recommender system. Appl Intell 39, 1–13 (2013). https://doi.org/10.1007/s10489-012-0389-1

Download citation

Keywords

  • Semantic information extraction
  • Social network analysis
  • Expert recommender system
  • Knowledge management