Guidelines for Web Search Engines: From Searching and Filtering to Interface

  • Markos Papoutsidakis
  • Stavroula Sampati
  • Ioannis Ledakis
  • Afroditi Sotiropoulou
  • Stavros Giannopoulos

In this paper we address a set of important guidelines that web search engines should follow in order to become effective. We refer to the importance of semantic web in the section of search engines which comes up from the better set-up that ontologies offer. Moreover, some of the most known and adaptive learning techniques are described in order to personalize web search engines, including methods for implicit and explicit feedback. In addition, we focus on how these methods can coexist so as to achieve high performance and we investigate the role of metadata in web searching in order to detect user interests and improve the information filtering procedure. Finally, we propose how these can be combined and presented into an interface, which will be considered as user friendly.


Search Engine User Profile Relevance Feedback Query Expansion User Interest 
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.
    Cetintemel U., Franklin M. and Giles L.: Flexible user profiles for large scale data delivery. Technical report CS-TR-4005, Computer Science Department, University of Maryland, (1999)Google Scholar
  2. 2.
    Kelly D. and Teevan J.: Implicit Feedback for Inferring User Preference: A Bibliography. SIGIR Forum, vol. 37 (2003) 18–28CrossRefGoogle Scholar
  3. 3.
    Leporini B., Andronico P. and Buzzi M.: Designing search engine user interfaces for the visually impaired. ACM International Conference Proceeding Series. Proceedings of the international cross-disciplinary workshop on Web accessibility (2004)Google Scholar
  4. 4.
    Gasparetti F. and Micarelli A.: Exploiting Web Browsing Histories to Identify User Needs. In: Proc. International Conference on Intelligent User Interfaces IUI 2007, Hawaii, January (2007) 28–31Google Scholar
  5. 5.
    Claypool, M., Le, P., Waseda M. and Brown D.: Implicit interest indicators. In: Proceedings of the 6th International Conference on Intelligent User Interfaces (IUI '01), USA, (2001) 33–40Google Scholar
  6. 6.
    Shen, X., Tan, B., and Zhai, C.: Context-sensitive information retrieval using implicit feedback. In: Proc. of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2005), Salvador, Brazil (2005) 43–50Google Scholar
  7. 7.
    Paul-Alexandru Chirita, Claudiu Firan and Wolfgang Nejdl: Summarizing Local Context to Personalize Global Web Search. In: Proceedings of the 15th ACM International CIKM Conference on Information and Knowledge Management, Arlington, United StatesGoogle Scholar
  8. 8.
    Salton G. and Buckley C.: Improving retrieval performance by relevance feedback. JASIS 41, 4 (1990) 288–297CrossRefGoogle Scholar
  9. 9.
    Page L., Brin S., Motwani R. and T. Winograd. The PageRank citation ranking: Bringing order to the Web. Technical report, Stanford University Database Group (1998)Google Scholar
  10. 10.
    Haveliwala T.: Topic-sensitive pagerank. In: Proceedings of the Eleventh International World Wide Web Conference, Honolulu, Hawaii (2002)Google Scholar
  11. 11.
    Jeh G. and Widom J.: Scaling personalized web search. In: Proceedings of the 12th Intl. World Wide Web Conference (2003)Google Scholar
  12. 12.
    Sugiyama K., Hatano K., and Yoshikawa M.: Adaptive web search based on user profile constructed without any effort from users. In: Proceedings of the 13th international conference on World Wide Web (2004) 675–684Google Scholar
  13. 13.
    Chirita P.-A., Nejdl W., Paiu R. and Kohlschütter Chr.: Using ODP metadata to personalize search. In Proc. of the 28th Intl. ACM SIGIR Conf. (2005)Google Scholar
  14. 14.
    Joachims T., Granka L., Pan B., Hembrooke H. and Gay G.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of SIGIR (2005)Google Scholar
  15. 15.
    Granka L., Joachims T., and Gay G.: Eye-Tracking Analysis of User Behavior in WWW Search. In: Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR) (2004)Google Scholar
  16. 16.
    Shen Xuehua: User-Centered Adaptive Information Retrieval. In: Proceedings of ACM Conference on Research and Development on Information Retrieval (SIGIR) (2006)Google Scholar
  17. 17.
    Doulaverakis C., Nidelkou E., Gounaris A. and Kompatsiaris Y.: An Ontology and Content-Based Search Engine For Multimedia Retrieval, 10th East-European Conference on Advances in Databases and Information Systems, ADBIS 2006, Thessaloniki, Hellas (2006)Google Scholar
  18. 18.
    White R.W., Ruthven I. and Jose J.M.: A study of factors affecting the utility of implicit relevance feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil (2005)Google Scholar
  19. 19.
    W3C http://www.w3.orgGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Computer Engineering and Informatics DepartmentUniversity of PatrasPatrasGreece
  2. 2.Open Research Society

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