Skip to main content

Web Information Personalization: Challenges and Approaches

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2822))

Abstract

As the number of web pages increases dramatically, the problem of the information overload becomes more severe when browsing and searching the WWW. To alleviate this problem, personalization becomes a popular remedy to customize the Web environment towards a user’s preference. To date, recommendation systems and personalized web search systems are the most successful examples of Web personalization. By focusing on these two types of systems, this paper reviews the challenges and the corresponding approaches proposed in the past ten years.

This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0082826, and unrestricted cash gifts from Microsoft, NCR, and Okawa Foundation.

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Shahabi, C., Banaei-Kashani, F., Chen, Y.-S., McLeod, D.: Yoda: An Accurate and Scalable Web-based Recommendation System. In: Proceedings of Sixth International Conference on Cooperative Information Systems (2001)

    Google Scholar 

  2. Shahabi, C., Chen, Y.-S.: An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback. Distributed and Parallel Databases 14, 173–192 (2003)

    Article  Google Scholar 

  3. Moukas, A.: Amalthea: Information discovery and filtering using a multiagent evolving ecosystem. In: Proceedings of 1st Int. Conf. on The Practical Applications of Intelligent Agents and MultiAgent Technology (1996)

    Google Scholar 

  4. Sheth, B., Maes, P.: Evolving Agents for Personalized Information Filtering. In: Proceedings of the Ninth IEEE Conference on Artificial Intelligence for Applications (1993)

    Google Scholar 

  5. Holland, J.: Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  6. Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3) (1997)

    Google Scholar 

  7. Shahabi, C., Zarkesh, A.M., Adibi, J., Shah, V.: Knowledge Discovery from Users Web Page Navigation. In: Proceedings of the IEEE RIDE 1997 Workshop (1997)

    Google Scholar 

  8. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of Dimensionality Reduction in Recommender System – A Case Study. In: Proceedings of ACM WebKDD 2000 Web Mining for e-Commerce Workshop (2000)

    Google Scholar 

  9. Kitts, B., Freed, D., Vrieze, M.: Cross-sell, a fast promotion-tunable customeritem recommendation method based on conditionally independent probabilities. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 337–446 (2000)

    Google Scholar 

  10. Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

    Google Scholar 

  11. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for e-Commerce. In: Proceedings of ACM e-Commerce 2000 Conference (2000)

    Google Scholar 

  12. Balabanovi, M., Shoham, Y.: Fab, content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  13. Balabanovi, M.: An Adaptive Web page Recommendation Service. In: Proceedings of Autonomous Agents, pp. 378–385 (1997)

    Google Scholar 

  14. Kohrs, A., Merialdo, B.: Using category-based collaborative filtering in the Active WebMuseum. In: Proceedings of IEEE International Conference on Multimedia and Expo, vol. 1, pp. 351–354 (2000)

    Google Scholar 

  15. Lieberman, H., Dyke, N., Vivacqua, A.: Let’s Browse, A Collaborative Browsing Agent. Knowledge-Based Systems 12, 427–431 (1999)

    Article  Google Scholar 

  16. Shardanand, U., Maes, P.: Social Information Filtering, Algorithm for automating ”Word of Mouth”. In: Proceedings on Human factors in computing systems, pp. 210–217 (1995)

    Google Scholar 

  17. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens, An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM conference on Cumputer-Supported Cooperative Work, pp. 175–186 (1994)

    Google Scholar 

  18. Good, N., Schafer, J., Konstan, J., Borchers, J., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: Proceedings of the 1999 Conference of the American Association of Artifical Intelligence, pp. 439–446 (1999)

    Google Scholar 

  19. Pazzani, M., Billsus, D.: Learning and Revising User profiles: The Indentification of Interesting Web Sites. Machine Learning 27, 313–331 (1997)

    Article  Google Scholar 

  20. Tan, A., Teo, C.: Learning User Profiles for Personalized Information Dissemination. In: Proceedings of Int’l Joint Conf. on Neural Network, pp. 183–188 (1998)

    Google Scholar 

  21. Lam, W., Mukhopadhyay, S., Mostafa, J., Palakal, M.: Detection of Shifts in User Interests for Personalized Information Filtering. In: Proceedings of the 19th Int’l ACM-SIGIR Conf. on Research and Development in Information Retrieval, pp. 317–325 (1996)

    Google Scholar 

  22. Goldberg, D.E.: Genetic Algorithms in Search, Optimisation, and Machine Learning. Addison-Wesley, Wokingham (1989)

    Google Scholar 

  23. Shahabi, C., Banaei-Kashani, F., Faruque, J., Faisal, A.: Feature Matrices: A Model for Efficient and Anonymous Web Usage Mining. In: Proceedings of EC-Web (2001)

    Google Scholar 

  24. Shahabi, C., Banaei-Kashani, F., Faruque, J.: A Reliable, Efficient, and Scalable System forWeb Usage Data Acquisition. In: WebKDD 2001 Workshop in conjunction with the ACM-SIGKDD (2001)

    Google Scholar 

  25. Fagin, R.: Combining Fuzzy Information from Multiple Systems. In: Proceedings of Fifteenth ACM Symposyum on Principles of Database Systems (1996)

    Google Scholar 

  26. Hunter, A.: Sugal Programming manual (1995), http://www.trajan-software.demon.co.uk/sugal.htm

  27. Wu, L., Faloutsos, C., Sycara, K., Payne, T.: FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In: Proceedings of Int’l. Conf. on Very Large Data Bases (2000)

    Google Scholar 

  28. Knorr, E., Ng, R., Tucakov, V.: Distance-Based Outliers: Algorithms and Applications. The VLDB Journal 8(3), 237–253 (2000)

    Article  Google Scholar 

  29. Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. Communications of the ACM 43(8), 142–151 (2000)

    Article  Google Scholar 

  30. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Web Data Mining: Effective personalization based on association rule discovery from web usage data. In: Proceeding of the Third International Workshop on Web Information and Data Management (2001)

    Google Scholar 

  31. Rui, Y., Huang, T., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 644–655 (1998)

    Article  Google Scholar 

  32. Knuth, D.: Seminumerical Algorithm. The Art of Computer Programming 2 (1997)

    Google Scholar 

  33. Lyman, P., Varian, H.R.: How Much Information (2000), Retrieved from http://www.sims.berkeley.edu/research/projects/how-much-info/internet.html

  34. Google: Google Technology (2003), Retrieved from http://www.google.com/technology/

  35. Jeh, G., Widom, J.: Scaling Personalized Web Search. In: Proceedings of the 12th International World Wide Web Conference (2003)

    Google Scholar 

  36. Haveliwala, T.H.: Topic-sensitive PageRank. In: Proceedings of the 11th International World Wide Web Conference (2002)

    Google Scholar 

  37. Chau, M., Zeng, D., Chen, H.: Personalized Spiders for Web Search and Analysis. In: Proceedings of ACM/IEEE Joint Conference on Digital Libraries (2001)

    Google Scholar 

  38. Liu, F., Yu, C.T., Meng, W.: Personalized web search by mapping user queries to categories. In: Proceedings of CIKM (2002)

    Google Scholar 

  39. Chen, L., Sycara, K.: WebMate: A Personal Agent for Browsing and Searching. In: Proceedings of the 2nd International Conference on Autonomous Agents (1998)

    Google Scholar 

  40. Tanudjaja, F., Mui, L.: Persona: a contextualized and personalized web search. In: 35th Annual Hawaii International Conference on System Sciences (2002)

    Google Scholar 

  41. Lawrence, S., Giles, C.L.: Accessibility of Information on the Web. Nature 400, 107–109 (1999)

    Article  Google Scholar 

  42. Scime, A., Kerschberg, L.: WebSifter: An Ontology-Based Personalizable Search Agent for the Web. In: Proceedings of International Conference on Digital Libraries: Research and Practice (2000)

    Google Scholar 

  43. Zhu, S., Deng, X., Chen, K., Zheng, W.: Using Online Relevance Feedback to Build Effective Personalized Metasearch Engine. In: Proceedings of Second International Conference on Web Information Systems Engineering (2001)

    Google Scholar 

  44. Glover, E., Lawrence, S., Birmingham, W.P., Giles, C.L.: Architecture of a Metasearch Engine that Supports User Information Needs. In: Proceedings of Eighth International Conference on Information and Knowledge Management (1999)

    Google Scholar 

  45. Glover, E., Flake, G.W., Lawrence, S., Birmingham, W.P., Kruger, A., Giles, C. L., Pennock, D.M.: Improving Category Specific Web Search by Learning Query Modifications. In: Proceedings of Symposium on Applications and the Internet (2001)

    Google Scholar 

  46. Chen, Y.-S., Shahabi, C., Burns, G.: Two-Phase Decision Fusion Based On User Preferences. submitted for reviewing (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shahabi, C., Chen, YS. (2003). Web Information Personalization: Challenges and Approaches. In: Bianchi-Berthouze, N. (eds) Databases in Networked Information Systems. DNIS 2003. Lecture Notes in Computer Science, vol 2822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39845-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-39845-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20111-3

  • Online ISBN: 978-3-540-39845-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics