Skip to main content
SpringerLink
Log in

Personalisation of Web Search

  • Conference paper
Intelligent Techniques for Web Personalization (ITWP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3169))

Included in the following conference series:

Abstract

The availability of web search has revolutionised the way people discover information, yet as search services maintain larger and larger indexes they are in danger of becoming a victim of their own success. Many common searches can return a vast number of web pages, many of which will be irrelevant to the searcher, and of which only about ten or twenty of the top-ranked results will be browsed. The problem is that while pages returned by a search may be relevant to the keywords entered, the keywords generally give only a partial expression of the searcher’s information need. Personalised web search takes keywords from the user as an expression of their information need, but also uses additional information about the user (such as their preferences, community, location or history) to assist in determining the relevance of pages.

There are many approaches to providing personalised web search, each with the aim of returning the results most relevant to the user ranked highest. The features that distinguish the approaches are the kind of information about the user that is used, the level of interaction with the user (explicit or implicit collection of data), how the information is stored (client-side or server-side), the algorithm used to incorporate the information about the user into the search and how information is presented to the user (mobile devices present some unique challenges in this respect). Some of these personalisation methods stem from techniques previously used in traditional information retrieval, whilst others are unique to the web environment.

This chapter describes the many techniques that have been applied to adapt the web search process to the individual user. We also present a novel system that we are developing, which uses a client-side user profile to provide a personalised ranking of results from multiple search portals. We conclude with a brief consideration of the future of personalised search and how it may affect the development of the web.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andersson, A., Larsson, N.J., Swanson, K.: Suffix trees on words. Algorithmica 23, 246–260 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  2. Armstrong, R., Freitag, D., Joachims, T., Mitchell, T.: WebWatcher: A learning apprentice for the World Wide Web. In: AAAI Spring Symposium on Information Gathering, Stanford, CA (March 1995)

    Google Scholar 

  3. August, K.G., Hansen, M.H., Shriver, E.: Mobile web searching. Bell Labs Technical Journal 6(2), 84–98 (2002)

    Article  Google Scholar 

  4. Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press and Addison-Wesley, Reading (1999)

    Google Scholar 

  5. Balabanović, M.: An adaptive web page recommendation service. In: Proceedings of the First International Conference on Autonomous Agents, pp. 378–385. ACM Press, New York (1997)

    Chapter  Google Scholar 

  6. Belew, R.K.: Finding Out About. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  7. Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Modeling and User-Adapted Interaction 10, 147–180 (2000)

    Article  Google Scholar 

  8. Borodin, A., Roberts, G.O., Rosenthal, J.S., Tsaparas, P.: Finding authorities and hubs from link structures on the world wide web. In: world wide web, pp. 415–429 (2001)

    Google Scholar 

  9. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  10. Carreira, R., Crato, J.M., Gonçalves, D., Jorge, J.: Evaluating adaptive user profiles for news classification. In: Proceedings of the 9th International Conference on Intelligent User Interface, Madeira, Funchal, Portugal, pp. 206–212. ACM Press, New York (2004)

    Google Scholar 

  11. Chen, L., Sycara, K.: WebMate: A personal agent for browsing and searching. In: Sycara, K.P., Wooldridge, M. (eds.) Proceedings of the 2nd International Conference on Autonomous Agents (Agents 1998), pp. 132–139. ACM Press, New York (1998)

    Chapter  Google Scholar 

  12. Chen, Z., Meng, X.: MARS: Applying multiplicative adaptive user preference retrieval to web search. In: Proceedings of the International Conference on Internet Computing 2002 (IC 2002), Las Vegas, Nevada, USA, pp. 643–648 (2002)

    Google Scholar 

  13. Chen, Z., Zhu, B.: Some formal analysis of Rocchio’s similarity-based relvance feedback algorithm. In: International Symposium on Algorithms and Computation, pp. 108–119 (2000)

    Google Scholar 

  14. Chirita, P., Olmedilla, D., Nejdl, W.: PROS: a personalized ranking platform for web search. In: Proceedings of the 3rd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Eindhoven, Netherlands (August 2004)

    Google Scholar 

  15. Cui, H., Wen, J., Jian-Yun, N., Wei-Ying, M.: Query expansion by mining user logs. IEEE Transactions on Knowledge and Data Engineering 15(4) (July/August 2003)

    Google Scholar 

  16. Direct Hit. Popularity-based search, http://www.directhit.com/

  17. Edwards, P., Steele, M., Clare, S.E., Johns, D.M.: Exploiting a web cache for user recommendations. In: Proceedings of Agents 2000 Workshop on Agent-Based Recommender Systems (WARS 2000) (2000)

    Google Scholar 

  18. Eurekster (2004), http://www.eurekster.com

  19. Findory.com. Personalized news (2004), http://www.findory.com

  20. Flake, G., Glover, E., Lawrence, S., Giles, C.L.: Extracting query modifications from nonlinear SVMs. In: International World Wide Web Conference, Honolulu, Hawaii, May 7–11, 2002, pp. 317–324 (2002)

    Google Scholar 

  21. Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.: Self-organization of the web and identification of communities. IEEE Computer 35(3), 66–71 (2002)

    Article  Google Scholar 

  22. Freyne, J., Smyth, B., Coyle, M., Balfe, E., Briggs, P.: Further experiments on collaborative ranking in community-based web search. Artificial Intelligence Review (to appear, 2004)

    Google Scholar 

  23. Gibson, D., Kleinberg, J., Raghavan, P.: Inferring web communities from link topology. In: Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia: Links, Objects, Time and Space — Structure in Hypermedia Systems, pp. 225–234. ACM Press, New York (1998)

    Chapter  Google Scholar 

  24. Glover, E.J., Lawrence, S., Gordon, M.D., Birmingham, W.P., Giles, C.L.: Web search—your way. Communications of the ACM 44(12), 97–102 (2001)

    Article  Google Scholar 

  25. Google Directory. The web organized by topic into categories, http://directory.google.com/

  26. Google Labs. Google personalized (beta), http://labs.google.com/personalized

  27. Google Local (beta). Find local businesses and services on the web, http://local.google.com

  28. Harman, D.: Relevance feedback revisited. In: Proceedings of SIGIR 1992, the 15th ACM International Conference on Research and Development in Information Retrieval, pp. 1–10 (1992)

    Google Scholar 

  29. Haveliwala, T.H.: Topic-sensitive PageRank. In: Proceedings of the Eleventh International World Wide Web Conference (WWW 2002), Honolulu, Hawaii (May 2002)

    Google Scholar 

  30. Jeh, G., Widom, J.: Scaling personalized web search. In: Proc. WWW 2003, Budapest, Hungary, May 20-24 (2002)

    Google Scholar 

  31. Jennings, A., Higuchi, H.: A personal news service based on a user model neural network. IEICE Transactions on Information and Systems 75(2), 198–209 (1992)

    Google Scholar 

  32. Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TF-IDF for text categorization. In: International Conference on Machine Learning, pp. 143–151 (1997)

    Google Scholar 

  33. Joachims, T., Freitag, D., Mitchell, T.M.: WebWatcher: A tour guide for the World Wide Web. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 770–777. Morgan Kaufmann publishers Inc., San Mateo (1997)

    Google Scholar 

  34. Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: A bibliography. SIGIR Forum 37(2), 18–28 (2003)

    Article  Google Scholar 

  35. Khopkar, Y., Spink, A., Giles, C.L., Shah, P., Debnath, S.: Search engine personalization: An exploratory study. First Monday 8(7) (July 2003), See: http://www.firstmonday.org/issues/issue8_7/khopkar/

  36. Kim, M.W., Kim, E.J., Ryu, J.W.: A collaborative recommendation based on neural networks. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 425–430. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  37. Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  38. Kritikopoulos, A., Sideri, M.: The Compass Filter: Search engine result personalisation using web communities. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  39. Kumar, S., Ertöz, L., Singhal, S., Oztekin, B.U., Han, E., Kumar, V.: Personalized profile based search interface with ranked and clustered display. Technical report, University of Minnesota, Department of Computer Science (2001), https://wwws.cs.umn.edu/tech_reports/listing/tr2001/01-023.pdf

  40. Lawrence, S.: Context in web search. IEEE Data Engineering Bulletin 23(3), 25–32 (2000)

    Google Scholar 

  41. Lieberman, H.: Letizia: An agent that assists web browsing. In: Mellish, C.S. (ed.) Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 1995), Montreal, Quebec, Canada, pp. 924–929. Morgan Kaufmann publishers Inc., San Mateo (1995)

    Google Scholar 

  42. Lieberman, H., Selker, T.: Out of context: Computer systems that adapt to, and learn from, context. IBM Systems Journal 39 (2000)

    Google Scholar 

  43. Liu, F., Yu, C., Meng, W.: Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering 16(1) (January 2004)

    Google Scholar 

  44. Liu, J., Wong, C.K., Hui, K.K.: An adaptive user interface based on personalized learning. IEEE Intelligent Systems 18(2), 52–57 (2003)

    Article  Google Scholar 

  45. Lycos, Inc. HotBot, http://www.hotbot.com

  46. Manber, U., Patel, A., Robison, J.: Experience with personalization on Yahoo! Communications of the ACM 43(8), 35–39 (2000)

    Article  Google Scholar 

  47. Meng, X., Chen, Z., Spink, A.: A multiplicative gradient descent search algorithm fo user preference retrieval and its application to web search. In: Proceedings of the International Conference on Information Technology: Computers and Communications, Las Vegas, Nevada, April 28-30 (2003)

    Google Scholar 

  48. MSN. Newsbot (beta) (2004), http://uk.newsbot.msn.com

  49. Netscape. Open Directory Project, http://dmoz.org

  50. Nichols, D.M.: Implicit ratings and filtering. In: Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, Hungary, pp. 31–36 (1997)

    Google Scholar 

  51. Pazzani, M.J., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying interesting web sites. In: AAAI/IAAI, vol. 1, pp. 54–61 (1996)

    Google Scholar 

  52. Pitkow, J.E., Schütze, H., Cass, T.A., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., Breuel, T.M.: Personalized search. Communications of the ACM 45(9), 50–55 (2002)

    Article  Google Scholar 

  53. Pretschner, A., Gauch, S.: Ontology based personalized search. In: Proceedings of the 11th International Conference on Tools with Artificial Intelligence (ICTAI), Chicago, November 1999, pp. 391–398 (1999)

    Google Scholar 

  54. Smyth, B., Rafter, R., Bradley, K.: Automated collaborative filtering applications for online recruitment services. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) AH 2000. LNCS, vol. 1892, pp. 363–368. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  55. Richardson, M., Domingos, P.: The Intelligent Surfer: Probabilistic combination of link and content information in PageRank. In: Advances in Neural Information Processing Systems, 14. MIT Press, Cambridge (2002)

    Google Scholar 

  56. Rocchio, J.J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System - Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)

    Google Scholar 

  57. Salton, G. (ed.): The SMART Retrieval System - Experiments in Automatic Document Processing. Prentice Hall, Englewood Cliffs (1971)

    Google Scholar 

  58. Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating “word of mouth”. In: Proceedings of the CHI 1995 Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press, New York (1995)

    Google Scholar 

  59. Sullivan, D.: Eurekster launches personalized social search. SearchEngineWatch (January 21, 2004), See: http://searchenginewatch.com/searchday/article.php/3301481

  60. Sunderam, A.V.: Automated personalisation of internet news. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 348–357. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  61. Topix.net. Pick your Topix, get the news, http://www.topix.net/

  62. Vivísimo Inc. Vivísimo clustering engine, http://vivisimo.com

  63. Weld, D.S., Anderson, C., Domingos, P., Etzioni, O., Gajos, K., Lau, T., Wolfman, S.: Automatically personalizing user interfaces. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico (August 2003)

    Google Scholar 

  64. White, R.W., Jose, J.M., Ruthven, I.: Using top-ranking sentences for web search result presentation. In: Poster Proceedings of the 12th International World Wide Web Conference (WWW 2003), Budapest, Hungary, May 20-24 (2003)

    Google Scholar 

  65. White, R.W., Ruthven, I., Jose, J.M.: The use of implicit evidence for relevance feedback in web retrieval. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) ECIR 2002. LNCS, vol. 2291, Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  66. Wong, S.K.M., Yao, Y.Y., Salton, G., Buckley, C.: Evaluation of an adaptive linear model. Journal of the American Society for Information Science and Technology 42, 723–730 (1991)

    Article  Google Scholar 

  67. Xi, W., Zhang, B., Chen, Z., Lu, Y., Yan, S., Ma, W.Y., Fox, E.A.: Link Fusion: A unified link analysis framework for multi-type inter-related data objects. In: Proceedings of the Thirteenth International World Wide Web Conference (WWW 2004), New York, U.S.A., May 19-22 (2004)

    Google Scholar 

  68. Yahoo! Inc. Yahoo!, http://www.yahoo.com

  69. Yahoo! Inc. Yahoo! Local, http://local.yahoo.com/

  70. Zamir, O., Etzioni, O.: Grouper: A dynamic clustering interface to Web search results. Computer Networks (Amsterdam, Netherlands: 1999) 31(11–16), 1361–1374 (1999)

    Google Scholar 

  71. Zhu, T., Greiner, R., Häubl, G.: An effective complete-web recommender system. In: Proceedings of the Twelfth International World Wide Web Conference (WWW 2003), Budapest, Hungary, May 20-24 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Information Systems, Birkbeck, University of London, UK

    Kevin Keenoy & Mark Levene

Authors
  1. Kevin Keenoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Levene
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Web Intelligence School of Computing, DePaul University, Chicago, Illinois, USA

    Bamshad Mobasher

  2. Department of Computer Science, University of Warwick, Coventry, United Kingdom

    Sarabjot Singh Anand

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Keenoy, K., Levene, M. (2005). Personalisation of Web Search. In: Mobasher, B., Anand, S.S. (eds) Intelligent Techniques for Web Personalization. ITWP 2003. Lecture Notes in Computer Science(), vol 3169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577935_11

Download citation

  • DOI: https://doi.org/10.1007/11577935_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29846-5

  • Online ISBN: 978-3-540-31655-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation