Integrating Online Social Network Analysis in Personalized Web Search

  • Omair ShafiqEmail author
  • Tamer N. Jarada
  • Panagiotis Karampelas
  • Reda Alhajj
  • Jon G. Rokne
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)


With the emergence of high speed internet applications and advanced Web 2.0 based Rich Internet Applications (i.e., blogs, wikis, etc.), it has become much easier for the users to publish data over the Web. This brings a challenge for the Web search solutions to let individual users find the right information as per their preferences, because traditional Web search engines have been built on “one size fits for all” concept. Different users of the Web may have different preferences. Search results for the same query raised by different users may differ in priority for individual users. In this book chapter, we present the extended version and results of our proposal on community-aware personalized Web search. It is quite challenging to know the preferences of the users by the search engines. We have designed and developed our unique approach of finding the preferences of users from the relevant parts of the user’s social network and community. We believe that the information related to the queries posed by the users may have strong correlation with the relevant information in their social networks. In order to find out personal interest and social-context, we find (1) activities of users in their social-network, and (2) relevant information from user’s social networks, based on our proposed trust and relevance matrices. We have further developed a mechanism that extracts from user’s social network information to be used to re-rank search results from a search engine. We also have discussed the implementation and evaluation details of our proposed solution.


Social Network Search Result Betweenness Centrality National Football League Official Site 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Almeida, R.B., Almeida, V.A.F.: A community-aware search engine. In: Proceedings of International World Wide Web Conference (WWW 2004). New YorkGoogle Scholar
  2. 2.
    Anick, P.: Using terminological feedback for web search refinement: a log-based study. In Proceedings of WWW 2004, New York, pp. 89–95 (2004)Google Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American Magazine (2001).
  4. 4.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of WWW, Santa Clara (1997)Google Scholar
  5. 5.
    Carminati, B., Ferrari, E., Perego, A.: Combining social networks and semantic web technologies for personalizing web access. In: Proceedings of the 4th International Conference on Collaborative Computing (CollaborateCom 2008), Orlando (2008)Google Scholar
  6. 6.
    Chen, H., Finin, T., Joshi, A.: An ontology for context-aware pervasive computing environments. Special Issue on Ontologies for Distributed Systems, Knowledge Engineering (2004)Google Scholar
  7. 7.
    Dou, Z., Song, R., Wen, J.R., Yuan, X.: Evaluating the effectiveness of personalized web search. IEEE Trans. Knowl. Data Eng. 21(8), 1178–1190 (2009)CrossRefGoogle Scholar
  8. 8.
    Freeman, L.: The development of social network analysis. Empirical Press, Vancouver (2006)Google Scholar
  9. 9.
    Google Personal (2009).
  10. 10.
    Granovetter, M.S.: The strength of weak ties. Am. J. Soc. 78, 1360–1380 (1973)CrossRefGoogle Scholar
  11. 11.
    Heath, T., Motta, E., Petre, M.: Computing word-of-mouth trust relationships in social networks from semantic Web and Web2.0 data sources. In: Proceedings of the Workshop on Bridging the Gap Between Semantic Web and Web 2.0, 4th European Semantic Web Conference (ESWC2007), Innsbruck (2007)Google Scholar
  12. 12.
    Jarvelin, K., Keklinen, J.: Ir evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2000, Athens (2000)Google Scholar
  13. 13.
    Jeh, G., Widom, J.: Scaling personalized web search. In: Proceedings of WWW 2003, pp. 271–279 (2003)Google Scholar
  14. 14.
    Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), Salvador (2005)Google Scholar
  15. 15.
    McKeown, K.R., Elhadad, N., Hatzivassiloglou, V.: Leveraging a common representation for personalized search and summarization in a medical digital library. In: Proceedings of ICDL 2003, pp. 159–170. New Delhi, India (2003)Google Scholar
  16. 16.
    Morris, M.R., Teevan, J., Bush, S.: Enhancing collaborative Web search with personalization – Groupization, smart splitting, and group hit-highlighting. In: Proceedings of CSCW 2008. SIGCHI/Association for Computing Machinery, New York (2008)Google Scholar
  17. 17.
    Parr, B.: BREAKING News: Google announces social search. October 21st, 2009.
  18. 18.
    Pattanasri, N., Jatowt, A., Tanaka, K.: Context-aware search inside e-learning materials using textbook ontologies. In: Advances in Data and Web Management. LNCS, vol. 4505/2009. Springer, Berlin (2009). doi: 10.1007/978-3-540-72524-4, ISBN:978-3-540-72483-4
  19. 19.
    Shafiq, O., Alhajj, R., Rokne, J.G.: Community aware personalized web search. In: Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010), Odense (2010)Google Scholar
  20. 20.
    Speretta, M., Gauch, S.: Personalizing search based on user search history. In: Proceedings of CIKM, Washington (2004)Google Scholar
  21. 21.
    Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from user. In: Proceedings of WWW, New York, pp. 675–684 (2004)Google Scholar
  22. 22.
    Tarjan, R.E.: A note on finding the bridges of a graph. Inf. Process. Lett. 2, 160–161 (1974)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Teevan, J., Dumais, S.T., Horvitz, E.: Beyond the commons: investigating the value of personalizing Web search. In: Proceedings of the Workshop on New Technologies for Personalized Information Access (PIA), Edinburgh (2005)Google Scholar
  24. 24.
    Teevan, J., Dumais, S.T., Horvitz, E.: Characterizing the value of personalizing search. In: Proceedings of SIGIR 2007, Amsterdam, pp. 757–756 (2007)Google Scholar
  25. 25.
    Zeng, Y., Ren, X., Qin, Y., Zhong, N., Huang, Z., Wang, Y.: Social relation based scalable semantic search refinement. In: Workshop on Scalable Semantic Data Processing, Asian Semantic Web Conference (ASWC 2009), 7th December 2009, Shanghai (2009)Google Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Omair Shafiq
    • 1
    Email author
  • Tamer N. Jarada
    • 2
  • Panagiotis Karampelas
    • 3
  • Reda Alhajj
    • 1
    • 3
    • 4
  • Jon G. Rokne
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.University of CalgaryCalgaryCanada
  3. 3.Department of Information TechnologyHellenic American UniversityManchesterUSA
  4. 4.Department of Computer ScienceGlobal UniversityBeirutLebanon

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