Skip to main content

METIOREW: An Objective Oriented Content Based and Collaborative Recommending System

  • Conference paper
  • First Online:
Hypermedia: Openness, Structural Awareness, and Adaptivity (AH 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2266))

Included in the following conference series:

Abstract

The size of Internet has been growing very fast and many documents appear every day in the Net. Users find many problems in obtaining the information that they really need. In order to help users in this task of finding relevant information, recommending systems were proposed. They give advice using two methods: the content-based method that extracts information from the already evaluated documents by the user in order to obtain new related documents; the collaborative method that recommends documents to the user based on the evaluation by users with similar information needs. In this paper we analyze some existing Web recommending systems and identify some problems which we try to solve in our system METIOREW.

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

Access this chapter

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. Armstrong, R., Freitag, D., Joachims, T., & Mitchell, T. (1995). “Webwatcher: A learning apprentice for the world wide web”. AAAI Spring Symposium on Information Gathering from Heterogeneous Distributed Environments

    Google Scholar 

  2. Balabanovic, M., & Shoham, Y. (1997). “Combining Content-Based and Collaborative Recommendation”. Communications of the ACM, 40(3)

    Google Scholar 

  3. Barra, M. (2000). "Distributed Systems for Group Adaptivity on the Web". Adaptive Hypermedia and Adaptive Web-Based Systems Springer-Verlag

    Google Scholar 

  4. Billsus, D., & Pazzani, M. (1998). “Learning Collaborative Information Filters”. Proceedings of the International Conference on Machine Learning Madison, Wisc.Morgan Kaufmann Publishers

    Google Scholar 

  5. Billsus, D., & Pazzani, M. (1999). “A Hybrid User Model for News Story Classification”. Proceedings of the Seventh International Conference on User Modeling (UM’ 99) Banff, Canada

    Google Scholar 

  6. Bueno, D., & David, A. A. “ METIORE: A Personalized Information Retrieval System”. 8 International Conference on User Modeling.UM’2001

    Google Scholar 

  7. Cotter, P., & Smyth, B. (2000). “WAPing the Web: Content Personalisation for WAPEnabled Devices”. Adaptive Hypermedia and Adaptive Web-Based Systems Springer-Verlag

    Google Scholar 

  8. Good, N., Schafer J., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., & Riedl, J. (1999). “Combining collaborative filtering with personal agents for better recommendations”. In Proceedings of the Sixteenth National Conferenceon Artificial Intelligence

    Google Scholar 

  9. Kononenko, I. (1990). "Comparison of Inductive and Naive Bayesian Learning Approaches to Automatic Knowledge Acquisition". Current Trends in Knowledge Adquisition, 190–197

    Google Scholar 

  10. Lieberman, H. (1995). " Letizia: An Agent That Assists Web Browsing". International Joint Conference on Artificial Intelligence Montreal, CA.

    Google Scholar 

  11. Mitchell, T. M. (1997). "Machine Learning". The McGraw-Hill Companies, Inc.

    Google Scholar 

  12. Nwana, H. (1996). "Software Agents: An Overview". Knowledge Engineering Review

    Google Scholar 

  13. Pazzani, M., Muramatsu, J., & Billsus, D. (1996). “Syskill & Webert: Identifying interesting web sites”. AAI Spring Symposium on Machine Learning in Information Access. URL= http://www.parc.xerox.com/istl/porjects/mlia/papers/pazzani.ps.

  14. Rafter, R., Bradley, K., & Smyth, B. (2000). “Automated Collaborative Filtering Applications for Online Recruitment Services”. Adaptive Hypermedia and Adaptive Web-Based Systems Italy Springer-Verlag

    Google Scholar 

  15. Rich E. (1979). "User Modeling via Stereotypes". International Journal of Cognitive Science, 3, 329–354

    Article  Google Scholar 

  16. Vel, O., & Nesbitt, S. (1997). “A Collaborative Filtering Agent System for Dynamic Virtual Communities on the Web”. URL= http://citeseer.nj.nec.com/de-collaborative.html.

  17. Versteegen, L. (2000). "The Simple Bayesian Classifier as a Classification Algorithm". URL= http://www.cs.kun.nl/nsccs/artikelen/leonv.ps.Z.

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bueno, D., Conejo, R., David, A.A. (2002). METIOREW: An Objective Oriented Content Based and Collaborative Recommending System. In: Reich, S., Tzagarakis, M.M., De Bra, P.M.E. (eds) Hypermedia: Openness, Structural Awareness, and Adaptivity. AH 2001. Lecture Notes in Computer Science, vol 2266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45844-1_28

Download citation

  • DOI: https://doi.org/10.1007/3-540-45844-1_28

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43293-7

  • Online ISBN: 978-3-540-45844-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics