ExpertRec: A Collaborative Web Search Engine

  • Jingyu Sun
  • Junjie Chen
  • Xueli Yu
  • Ning Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6988)


ExpertRec is a collaborative Web search engine, which is differ from current main search engine and allows users share search histories through a Web browser toolbar or a proxy browser. In addition, it can be taken as a novel social Web search engine and utilize expert’s search histories for building recommendations. In this paper, we give an anatomy of ExpertRec and specially introduce its architecture and core techniques. It includes two basic components: a client agent and a back-end server. The former is implemented as a Mozilla Firefox toolbar (a Firefox extension), which can integrate with mainstream search engines like Google, Yahoo!, et al., to meet users’ teamwork needs. And it allows users to generate high-quality tags, votes, comments over current Web including search histories, personal archival content in local host typically beyond the reach of existing Web 2.0 social tagging system. The latter is a CBR (case-based reasoning)-based recommendation engine and implemented according to some core techniques, such recommendation rules, a scalable method to identify search expertise based on a hierarchical user profile in order to improve users’ search quality, and so on. Finally, we give an evaluation and make conclusions.


Core Technique Client Agent Search History User Social Network Familiar Topic 
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.


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  1. 1.
    Amershi, S., Morris, M.: Cosearch: A system for co-located collaborative web search. In: CHI 2008, pp. 1647–1656 (2008)Google Scholar
  2. 2.
    Balog, K., Azzopardi, L., de Rijke, M.: Formal models for expert finding in enterprise corpora. In: Proceedings of SIGIR, pp. 43–55 (2006)Google Scholar
  3. 3.
    Cover, T.M., Thomas, J.A.: Elements of information theory, 1st edn. Wiley-InterScience, New York (1991)CrossRefzbMATHGoogle Scholar
  4. 4.
    Eduardo, D.: On clustering and evaluation of narrow domain short-text corpora. PhD thesis (2008)Google Scholar
  5. 5.
    Morris, M., Horvitz, E.: S3: Storable, shareable search. In: Baranauskas, C., Abascal, J., Barbosa, S.D.J. (eds.) INTERACT 2007. LNCS, vol. 4662, pp. 120–123. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Morris, M., Horvitz, E.: Searchtogether: An interface for collaborative web search. In: IUIST 2007, pp. 3–12 (2007)Google Scholar
  7. 7.
    Smyth, B., Champin, P.: The experience web: A case-based reasoning perspective. In: Grand Challenges for Reasoning from Experiences, Workshop at IJCA 2009, pp. 566–573 (2009)Google Scholar
  8. 8.
    Sun, J., Yu, X., Wang, R., Zhong, N.: A model for personalized web-scale case base maintenance. In: Liu, J., Wu, J., Yao, Y., Nishida, T. (eds.) AMT 2009. LNCS, vol. 5820, pp. 442–453. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Sun, J.Y., Yu, X.L., Zhong, N.: Collaborative web search utilizing experts’ experiences. In: Web Intelligence 2010, pp. 120–127 (2010)Google Scholar
  10. 10.
    von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17, 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yang, J., Zhong, N., Yao, Y.Y., Wang, J.: Local peculiarity factor and its application in outlier detection. In: KDD 2008, pp. 776–784 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jingyu Sun
    • 1
    • 2
  • Junjie Chen
    • 1
  • Xueli Yu
    • 1
  • Ning Zhong
    • 2
  1. 1.College of Computer Science and TechnologyTaiyuan University of TechnologyTaiyuanChina
  2. 2.International WIC InstituteBeijing University of TechnologyBeijingChina

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