Skip to main content
SpringerLink
Log in

A Taxonomy of Recommender Agents on the Internet

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have analyzed 37 different systems and their references andhave sorted them into a list of 8 basic dimensions. These dimensions arethen used to establish a taxonomy under which the systems analyzed areclassified. Finally, we conclude this paper with a cross-dimensionalanalysis with the aim of providing a starting point for researchers toconstruct their own recommender system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amazon (2001). http://www.amazon.com.

  • Armstrong, R., Freitag, D., Joachims, T. & Mitchell, T. (1995). WebWatcher: A Learning Apprentice for the World Wide Web. In 1995 AAAI Spring Symposium on Information Gathering from Heterogeneous Distributed Environments.

  • Asnicar, F. & Tasso, C. (1997). IfWeb: A Prototype of User Models Based Intelligent Agent for Document Filtering and Navigation in the World Wide Web. In Proceedings of UM'97. Sardinia, Italy: Chia Laguna.

    Google Scholar 

  • Balabanovic, M. & Shoham, Y. (1997). Combining Content-Based and Collaborative Recommendation. Communications of the ACM.

  • Basu, C., Hirsh, H. & Cohen, W. (1998). Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In Proceedings of AAAI'98, 714–720.

  • Berney, B. & Ferneley, E. (1999). Casmir: Information Retrieval Based on Collaborative User Profiling. In Proceedings of PAAM'99, 41–56. Lancashire: The Practical Application Company Ltd.

    Google Scholar 

  • Billsus, D. & Pazzani, M. J. (1998). Learning Collaborative Information Filters. In Proceedings of the International Conference on Machine Learning. Madison, WI: Morgan Kaufmann Publishers.

    Google Scholar 

  • Billsus, D. & Pazzani, M. J. (1999). A Hybrid User Model for News Classification. In Proceedings of UM'99, 99–108. Wien, New York: Springer-Verlag.

    Google Scholar 

  • Boone, G. (1998). Concept Features in RE:Agent, an Intelligent Email Agent. In The Second International Conference on Autonomous Agents (Agents' 98). Minneapolis/St. Paul.

  • Breese, J., Heckerman, D. & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Uncertainty in Artificial Intelligence: Proceedings of the 14th Conference, 43–52. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Buckley, C. & Salton, G. (1995). Optimization of Relevance Feedback Weights. In Fox, E., Ingwersen, P. & Fidel, R. (eds.) Proceedings of the Eighteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 351–357.

  • Buckley, C., Singhal, A., Mitra, M. & Salton, G. (1996). New Retrieval Approaches Using SMART. In Proceedings of TREC-4. NIST Special Publication.

  • Carroll, J. & Rosson, M. B. (1987). The Paradox of the Active User. In Carroll, J. M. (ed.) Interfacing Thought: Cognitive Aspects of Human-Computer Interaction, 26–28. Cambridge, MA: MIT Press.

    Google Scholar 

  • CDNow (2001). http://www.cdnow.com.

  • Chatterjee, P., Hoffman, D. L. & Novak, T. P. (1998). Modeling the Clickstream: Implications for Web-Based Advertising Efforts. Working Paper. Vanderbilt University.

  • Chen, L. & Sycara, K. (1998). Webmate: A Personal Agent for Browsing and Searching. In Proceedings of AGENTS' 98, 132–139. ACM.

  • Chen, Z., Meng, X., Zhu, B. & Fowler, R. (2000). WebSail: From On-Line Learning to Web Search. In Proceedings of the 2000 International Conference on Web Information Systems Engineering.

  • Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. In Machine Learning, Vol. 3, 261–283. The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Cohen, W. (1995). Fast Effective Rule Induction. In Proceedings of ML95, 115–123. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Cohen, W. & Singer, Y. (1999). A Simple, Fast, and Effective Rule Learner. In Proceedings of AAAI-99, 335–342.

  • Cooley, R., Tan, P. N. & Srivastava, J. (1999). WebSift: The Web Site Information Filter System. In Proceedings of the 1999 KDD Workshop on Web Mining. San Diego, CA: Springer-Verlag.

    Google Scholar 

  • Cost, S. & Salzberg, S. (1993). A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10: 57–78.

    Google Scholar 

  • Cunningham, P., Bergmann, R., Schmitt, S., Traphoner, R., Breen, S. & Smyth, B. (2001). WebSell: Intelligent Sales Assistants for the World Wide Web. In E-2001.

  • Duda, R. & Hart, P. (1973). Pattern Classification and Scene Analysis. New York: John Wiley & Sons, ISBN-0471223611.

    Google Scholar 

  • Goldberg, D., Nichols, D., Oki, B. M. & Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35: 61–70.

    Google Scholar 

  • 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 AAAI, Vol. 35, 439–446. AAAI Press.

    Google Scholar 

  • Greening, D. (1997). Building Consumer Trust with Accurate Product Recommendations. Likeminds White Paper LMWSWP-210-6966.

  • Hayes, C. & Cunningham, P. (1999). Smart Radio – a Proposal. In Trinity College Dublin, Computer Science, Technical Report, TCD-CS-1999-24.

  • Hayes, C. & Cunningham, P. (2000). Smart Radio: Building Music Radio on the Fly. In Proceedings of Expert Systems 2000 (ES2000). Cambridge, UK.

  • Hayes, C., Cunningham, P. & Smyth, B. (2001). A Case-Based Reasoning View of Automated Collaborative Filtering. In Trinity College Dublin, Computer Science, Technical Report, TCD-CS-2001-09.

  • Herlocker, J., Konstan, J., Borchers, A. & Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval.

  • Herlocker, J., Konstan, J. & Riedl, J. (2000). Explaining Collaborative Filtering Recommendations. In Proceedings of ACM 2000 Conference on Computer Supported Cooperative Work.

  • Hill, W., Stead, L., Rosenstein, M. & Furnas, G. (1995). Recommending and Evaluating Choices in a Virtual Community of Use. In Proceedings of CHI'95, 194–201. Denver.

  • Hofmann, T. & Puzicha, J. (1999). Latent Class Models for Collaborative Filtering. In Proceedings of IJCAI'99, 688–693. Stockholm, ISBN 1-55860-613-0.

  • Holte, R. C. & Yan, N. Y. (1996). Inferring What a User Is Not Interested In. In AAAI Spring Symp. on Machine Learning in Information Access. Stanford.

  • Huberman, B. & Kaminsky, M. (1996). Beehive: A System for Cooperative Filtering and Sharing of Information. Technical Report, Dynamics of Computation Group. Palo Alto, CA: Xerox, Palo Alto Research Center.

    Google Scholar 

  • Jennings, A. & Higuchi, H. (1993). A User Model Neural Network for a Personal News Service. User Modeling and User-Adapted Interaction 3: 1–25.

    Google Scholar 

  • Jensen, F. V. (1996). An Introduction to Bayesian Networks. New York: Springer.

    Google Scholar 

  • Joachims, T., Freitag, D. & Mitchell, T. (1997). WebWatcher: A Tour Guide for the World Wide Web. In Proceedings of IJCAI'97, 770–775. Nagoya, Japan.

  • Kamba, T., Bharat, K. & Albers, M. C. (1995). The Krakatoa Chronicle – an Interactive, Personalized, Newspaper on the Web. In Proceedings of the Fourth International World Wide Web Conference, 159–170.

  • Kobsa, A., Koenemann, J. & Pohl, W. (2001). Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review 16: 111–155.

    Google Scholar 

  • Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. & Riedl, J. (1997). Grouplens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40: 77–87.

    Google Scholar 

  • Koychev, I. (2000). Gradual Forgetting for Adaptation to Concept Drift. In Proceedings of ECAI 2000 Workshop Current Issues in Spatio-Temporal Reasoning.

  • Krulwich, B. (1997). LifeStyle Finder: Intelligent User Profiling Using Large-Scale Demographic Data. AI Magazine 18(2): 37–45.

    Google Scholar 

  • Krulwich, B. & Burkey, C. (1995). ContactFinder: Extracting Indications of Expertise and Answering Questions with Referrals. Working Notes of the 1995 Fall Symposium on Intelligent Knowledge Navigation and Retrieval, 85–91. Technical Report FS-95-03, The AAAI Press.

  • Krulwich, B. & Burkey, C. (1996). Learning User Information Interests Through Extraction of Semantically Significant Phrases. In Proceedings of he AAAI Spring Symposium on Machine Learning in Information Access. Stanford, CA.

  • Lang, K. (1995). NewsWeeder: Learning to Filter News. In Proceedings of the 12th International Conference on Machine Learning, 331–339. Lake Tahoe, CA.

  • Lieberman, H. (1995). Letizia: An Agent that Assists Web Browsing. In Proceedings of the IJCAI'95, 924–929.

  • Lieberman, H., Van Dyke, N. W. & Vivacqua, A. S. (1999). Let's Browse: A Collaborative Web Browsing Agent. In Proceedings of International Conference on Intelligent User Interfaces, 924–929.

  • Maes, P. (1994). Agents that Reduce Work and Information Overload. Communications of the ACM 37(7): 30–40.

    Google Scholar 

  • Maloof, M. A. & Michalski, R. S. (2000). Selecting Examples for Partial Memory Learning. Machine Learning 41: 27–52.

    Google Scholar 

  • Minio, M. & Tasso, C. (1996). User Modeling for Information Filtering on Internet Services: Exploiting an Extended Version of the UMT Shell. In UM96 Workshop on User Modeling for Information Filtering on the WWW. Kailua-Kona, Hawaii.

  • Mitchell, T., Caruana, R., Freitag, D., McDermott, J. & Zabowski, D. (1994). Experience with a Learning Personal Assistant. Communications of the ACM 37(7): 81–91.

    Google Scholar 

  • Mitchell, T. M., Mahadevan, S. & Steinberg, L. (1985). Leap: A Learning Apprentice for VLSI Design. In Proceedings of IJCAI'85, 573–580. Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  • Mladenic, D. (1996). Personal WebWatcher: Implementation and Design. Technical Report IJS-DP-7472, Department of Intelligent Systems. Slovenia: J. Stefan Institute.

    Google Scholar 

  • Mobasher, B., Cooley, R. & Srivastava, J. (2000). Automatic Personalization Based on Web Usage Mining. Communications of the ACM 43(8).

  • Morita, M. & Shinoda, Y. (1994). Information Filtering Based on User Behaviour Analysis and Best Match Text Retrieval. In Proceedings of SIGIR'94, 272–81. Dublin, Ireland: Springer-Verlag.

    Google Scholar 

  • Moukas, A. (1997). Amalthaea: Information Filtering and Discovery Using a Multiagent Evolving System. Journal of Applied AI 11(5): 437–457 (Dublin, Ireland, Springer-Verlag).

    Google Scholar 

  • Nichols, D. M. (1997). Implicit Rating and Filtering. In Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering, 31–36.

  • Orwant, L. J. (1995). Heterogeneous Learning in the Doppelganger User Modelling System. User Modelling and User Adapted Interaction 4(2): 107–130.

    Google Scholar 

  • Pazzani, M. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review.

  • Pazzani, M. & Billsus, D. (1997). Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27: 313–331 (Kluwer Academic Publishers).

    Google Scholar 

  • Pazzani, M., Muramatsu, J. & Billsus, D. (1996). Syskill & Webert: Identifying Interesting Web Sites. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, 54–61.

  • Potter, G. & Trueblood, R. (1988). Traditional, Semantic, and Hyper-Semantic Approaches to Data Modeling. IEEE Computer 21(6): 53–63.

    Google Scholar 

  • Pretschner, A. & Gauch, S. (1999). Ontology-Based Personalized Search. In Proceedings of ICTAI'99, 391–398.

  • Quinlan, J. R. (1983). Learning Efficient Classification Procedures and Their Application to Chess End Games. In Michalski, R. S., Carbonell, J. G. & Mitchell, T. M. (eds.) Machine Learning: An Artificial Intelligence Approach, 463–482.

  • Quinlan, J. R. (1994). The Minimum Description Length Principle and Categorical Theories. In Proceedings of ML'94. San Mateo: Morgan Kaufmann.

    Google Scholar 

  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. & Riedl, J. (1994). Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of ACM CSCW'94, 175–186.

  • Rich, E. (1979). User Modeling via Stereotypes. Cognitive Science 3: 329–354.

    Google Scholar 

  • Riordan, A. & Sorensen, H. (1995). An Intelligent Agent for High-Precision Information Filtering. In Proceedings of the CIKM-95 Conference.

  • Sakagami, H., Kamba, T. & Koseki, Y. (1997). Learning Personal Preferences on Online Newspaper articles for User Behaviors. In Proc. 6th Int. World Wide Web Conference, 291–300.

  • Salton, G. & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24(5): 513–523.

    Google Scholar 

  • Salton, G. & Buckley, C. (1990). Improving Retrieval Performance by Relevance Feedback. In Spark Jones and Willet (eds.) Readings in Information Retrieval, Vol. 24, No.5, 513–523. San Francisco, CA: Morgan Kauffman.

    Google Scholar 

  • Salton, G. & McGill, M. (1983). Introduction to Modern Information Retrieval. New York, NY: McGraw-Hill Publishing Company.

    Google Scholar 

  • Sarwar, B. M., Karypis, G., Konstan, J. A. & Riedl, J. (2000). Analysis of Recommender Algorithms for E-Commerce. In ACM E-Commerce 2000 Conference.

  • Schafer, J. B., Konstan, J. & Riedl, J. (2001). Electronic Commerce Recommender Applications. Journal of Data Mining and Knowledge Discovery 5: 115–152.

    Google Scholar 

  • Schwab, I., Kobsa, A. & Koychev, I. (2000). Learning about Users from Observation. In AAAI 2000 Spring Symposium: Adaptive User Interface.

  • Schwab, I., Kobsa, A. & Koychev, I. (2001). Learning User's Interests Through Positive Examples Using Content Analysis and Collaborative Filtering. Submitted.

  • Shardanand, U. (1994). Social Information Filtering for Music Recommendation. MIT EECS M. Eng. thesis, also TR-94-04. Learning and Common Sense Group. MIT Media Laboratory.

  • Shardanand, U. & Maes, P. (1995). Social Information Filtering: Algorithms for Automating ‘Word of Mouth’.... In Proceedings of CHI'95, 210–217.

  • Sheth, B. & Maes, P. (1993). Evolving Agents for Personalitzed Information Filtering. In Proceedings of the Ninth Conferece on Artificial Intelligence for Applications. IEEE Computer Society Press.

  • Sorensen, H. & McElligot, M. (1995). PSUN: A Profiling System for Usenet News. In CKIM' 95 Workshop on Intelligent Information Agents.

  • Sorensen, H., Riordan, A. O. & Riordan, C. O. (1997). Profiling with the INFORMER Text Filtering Agent. Journal of Universal Computer Science 3(8): 988–1006.

    Google Scholar 

  • Stefani, A. & Strappavara, C. (1998). Personalizing Access to Web Wites: The SiteIF Project. In Proceedings of HYPERTEXT'98.

  • Terveen, L. G. & Hill, W. (2001). Beyond Recommender Systems: Helping People Help Eachother. In Carroll, J. (ed.) HCI in the New Millennium. Addison Wesley.

  • Webb, G. & Kuzmycz, M. (1996). Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents' Competencies. User Modelling and User-Adapted Interaction 5: 117–150.

    Google Scholar 

  • Widmer, G. & Kubat, M. (1996). Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23: 69–101 (Kluwer Academic Publishers).

    Google Scholar 

  • Yan, T. W. & Garcia-Molina, H. (1995). Sift – a Tool for Wide-Area Information Dissemination. In Proceedings of the 1195 USENIX Technical Conference, 177–186.

Download references

Author information

Authors and Affiliations

  1. Agents Research Laboratory, Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus Montilivi, 17071, Girona, Spain

    Miquel Montaner, Beatriz López & Josep Lluís de la Rosa

Authors
  1. Miquel Montaner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beatriz López
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Josep Lluís de la Rosa
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Montaner, M., López, B. & de la Rosa, J.L. A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review 19, 285–330 (2003). https://doi.org/10.1023/A:1022850703159

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1022850703159

Search

Navigation