Skip to main content
SpringerLink
Log in

Information Filtering: Overview of Issues, Research and Systems

  • Published:
User Modeling and User-Adapted Interaction Aims and scope Submit manuscript

Abstract

An abundant amount of information is created and delivered over electronic media. Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation. Information filtering (IF) is one of the methods that is rapidly evolving to manage large information flows. The aim of IF is to expose users to only information that is relevant to them. Many IF systems have been developed in recent years for various application domains. Some examples of filtering applications are: filters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them only to suitable pages, filters for e-commerce applications that address products and promotions to potential customers only, and many more. The different systems use various methods, concepts, and techniques from diverse research areas like: Information Retrieval, Artificial Intelligence, or Behavioral Science. Various systems cover different scope, have divergent functionality, and various platforms. There are many systems of widely varying philosophies, but all share the goal of automatically directing the most valuable information to users in accordance with their User Model, and of helping them use their limited reading time most optimally. This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems. The paper defines a framework to classify IF systems according to several parameters, and illustrates the approach with commercial and academic systems. The paper describes the underlying concepts of IF systems and the techniques that are used to implement them. It discusses methods and measurements that are used for evaluation of IF systems and limitations of the current systems. In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and integration with digital libraries and Web repositories.

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

  • Abiteboul, S., Buenman, P. and Suciu, D.: 2000, Data on the Web: from Relations to Semistructured Data and XML, San Francisco: Morgan Kaufmann Publishers.

    Google Scholar 

  • Allen, R. B.: 1990, User models: Theory, methods and practice. Int. Journal of Man-Machine Studies 32, 511–543.

    Google Scholar 

  • Amazon: 2000, http://www.amazon.com.

  • Ambrosini, L., Cirillo, V. and Micarelli, A.: 1997, A hybrid architecture for user-adapted information filtering on the world wide web. In: A. Jameson, C. Paris, and C. Tasso (eds.): User Modeling: Proceedings of the Sixth International Conference, UM97, Chia Laguna, Sardinia, Italy, June 2–5, 1997, New York, Springer Wien, 59–61.

    Google Scholar 

  • Androutsopoulos, I., Koutsias, J., Chandrinos, K. V. and Spyropoulos, C. D.: 2000, An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: N. J. Belkin, P. Ingwersen and M.-K. Leong (eds.), Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece, July 24–28, 2000, 160–167.

  • Arnheim, L. A.: 1996, Summary of proceedings. Collaborative Filtering Workshop, March 16, Berkeley, CA, USA. http://www.sims.berkeley.edu/resources/collab/collab-report.html.

  • Arampatzis, A. T., van der Weide, T. P., Koster, C. H. A. and van Bommel, P.: 2000, Term selection for filtering based on distribution of terms over time, RIAO'2000 Conference Proceedings, Vol. 2, April 12–14, Paris, France, 1221–1237.

    Google Scholar 

  • Arms, W. Y.: 2000, Digital Libraries, Cambridge, Mass.: The MIT Press.

    Google Scholar 

  • Asnicar, F. A. and Tasso, C.: 1999, ifWeb: a Prototype of User Model-based intelligent agent for document filtering and navigation in the world wide web. Proceedings of the Workshop Adaptive Systems and User Modeling on the World Wide Web, Sixth International Conference on User Modeling, Chia Laguna, Sardinia, 2–5 June 1997.

  • Avery, C., Resnick, P. and Zeckhauser, R.: 1999, The market for evaluations. American Economics Review 89(3), 564–584.

    Google Scholar 

  • Baeza-Yates, R. and Ribeiro-Neto, B.: 1999, Modern Information Retrieval, New York: Addison-Wesley.

    Google Scholar 

  • Balabanovic, M.: 1998, Exploring versus exploiting when learning user models for text recommendation. User Modeling and User-Adapted Interaction 8(1/2), 71–102.

    Google Scholar 

  • Balabanovic, M. and Shoham, Y.: 1997, Fab: Content-based collaborative recommendation. Comm. of the ACM 40(3), 60–72.

    Google Scholar 

  • BarnesAndNoble: 2000, http://www.bn.com/.

  • Bates, M. E.: 1994, Electronic Clipping Services: A new life for SDIs. ONLINE, 18(4), 43–51.

    Google Scholar 

  • Beerud, S.: 1994, New — A personalized information filtering system. http://agents.www.media.mit.edu/groups/agents/papers/newt-thesis.

  • Belkin, N. J. and Croft, W. B.: 1992, Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM 35(12), 29–38.

    Google Scholar 

  • Ben-Hazez S. and Minel, J.: 2000, Designing tasks of identification of complex linguistic patterns used for text semantic filtering. RIAO2000, Conference Proceedings, Vol. 2, Paris, France, April 12–14.

  • Billsus, D. and Pazzani, M. J.: 1999a, A hybrid user model for news story classification. In: J. Kay (ed.): User Modeling: Proceedings of the Seventh International Conference, UM99, New York, Springer Wien, 99–108.

    Google Scholar 

  • Billsus, D. and Pazzani, M. J.: 1999b, A personal news agent that talks, learns and explains. Proceedings of the Third Annual Conference on Autonomous Agents, May 1–5, 1999, Seattle, WA, USA, 268–275.

  • Binkley, J. and Young, L.: 1995, An architecture for Internet information filtering, Journal of Intelligent Information Systems 5(2), 81–99.

    Google Scholar 

  • Bollacker, K. D., Lawrence, S. and Giles, C. L.: 1999, A system for automatic personalized tracking of scientific literature on the web, Proceedings of the fourth ACM Conference on Digital Libraries, August 11–14, 1999, Berkeley, CA, USA, 105–113.

  • Bollacker, K. D., Lawrence, S. and Giles, C. L.: 2000, Discovering relevant scientific literature on the web, IEEE Intelligent Systems March/April 2000 15(2), 42–47.

    Google Scholar 

  • Borchers, A. J., Herlocker, J., Konstan, J. and Riedel, J.: 1998, Ganging up on information overload. IEEE Computer 3(4), 106–108.

    Google Scholar 

  • Brajnik, G., Guida, G. and Tasso, C.: 1990, User modeling in expert man-machine interfaces: A case study in intelligent information retrieval. IEEE Transactions on Systems, Man and Cybernetics 20(1), 166–185.

    Google Scholar 

  • Brewester, K. and Art, M.: 1991, An Information system for corporation users: Wide area information servers (WAIS). Online 15(5), 56–60.

    Google Scholar 

  • BRM, Technologies: 2000, BackWeb — Push Service: http://www.backweb.com.

  • Calvi, L. and De Bra, P.: 1997, Proficiency-adapted, information browsing and filtering in hypermedia educational systems. User Modeling and User-Adapted Interaction 7(4), 257–277.

    Google Scholar 

  • Campione, M. and Wallalrath, K. 1998, The Java Tutorial, 2nd Ed., Reading, Mass.: Addison-Wesley.

    Google Scholar 

  • CDNOW: 2000, http://www.cdnow.com.

  • Chen, C.: 1999, Information Visualisation and Virtual Environments. London: Springer.

    Google Scholar 

  • Cingil, I., Dogac, A. and Azgin, A.: 2000, A broader approach to personalization. Communications of the ACM 43(8), 136–141.

    Google Scholar 

  • Communications Corporation: 2000, NewsClip news filtering language. http://www.clarinet.com/

  • Cranor, L. F. and LaMacchia, B. A.: 1998, Spam! Communications of the ACM 41(8), 74–83.

    Google Scholar 

  • Desjardins, G. and Godin, R.: 2000, Combining Relevance Feedback and Genetic Algorithms in an Internet Information Filtering Engine. RIAO'2000 Conference Proceedings, Vol. 2, April 12–14, 2000, Paris, France, 1676–1685.

    Google Scholar 

  • Dragan, R. V.: 1997, Advice from the web. PC Magazine, September 97, http://www.zdnet.com/pcmag/features/advice/open.htm.

  • Eachmovie data set 2000. http://www.sims.berkelev.edu/resources/mailing-lists/collab/0052-html.

  • Edmunds, A. and Morris, A.: 2000, The problem of information overload in business organizations: a review of the literature. International Journal of Information Management 20(2000), 17–28.

    Google Scholar 

  • Eliens, A.: 2000, Principles of Object-Oriented Software Development, 2nd Ed. Harlow, England: Addison-Wesley.

    Google Scholar 

  • Fisher, D. et al.: 2000, SWAMI: a framework for collaborative filtering algorithm development and evaluation. In: N. J. Belkin, P. Ingwersen and M.-K. Leong (eds.): Proceedings of the 23rd Annual International ACM SIGIR Conference, on Research and Development in Information Retrieval, Athens, Greece, July 24–28, 2000, 366–368.

  • Fleming, M. and Cohen, R.: 1999, User modeling in the design of interactive interface agents. Proceedings of the 7th International Conference (UM99), 67–76.

  • Foltz, P. W. and Dumais, S. T.: 1992, Personalized information delivery: An analysis of information filtering methods. Communications of the ACM 35(12), 51–60.

    Google Scholar 

  • Foltz, P. W.: 1990, Using latent semantic indexing for information filtering. ACM SIG-OIS, 40–47.

  • Foner, L. N.: 1997, Yenta: A multi-agent, referral-based matchmaking system. Proceedings of The First International Conference on Autonomous Agents (Agents'97), 301–307.

  • Frakes, W. B. and Baeza-Yates, R.: 1992, Information Retrieval: Data Structures and Algorithms. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Goeck, J. and Shavlik, J.: 2000, Learning users' interests by unobtrusively observing their normal behavior. Proceedings of the 2000 International Conference on Intelligent User Interfaces, New Orleans, LA.

  • Go (Infoseek): 2000, http://www.go.com/.

  • Goldberg, D., Nichols, D., Oki, B. and Terry, D.: 1992, Collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70.

    Google Scholar 

  • Gosling, J. and McGilton, H.: 1996, The Java Language Environment: A White Paper. May 96. http://java.sun.com/docs/white/langenv/.

  • Gurney, K.: 1996, An Introduction to Neural Networks. UCL Press, UK.

    Google Scholar 

  • Hanani, U. and Frank, A. J.: 2000a, Katsir: A framework for harvesting digital libraries on the web. ECIS 2000 Proceeding, Vienna, July, 2000, 306–312.

  • Hanani, U. and Frank, A. J.: 2000b, The parallel evolution of search engines and digital libraries: their convergence to the mega-portal. Proceedings of the 2000 Kyoto International Conference on Digital Libraries: Research and Practice (ICDL 2000), Kyoto, Japan, November 2000, 260–276.

  • Harman, D.: 1999, Overview of the eighth Text REtrieval Conference (TREC-8). Proceedings Eight Text Retrieval Conference (TREC-8), 1–19.

  • Hensley, P., Converse, D., Metral, M., Shardanand, U. and Myers, M.: 1997, Proposal for an Open Profiling Standard. W3C Note-OPS, June 97, http://www.w3.orp-/TR/NOTE-OPS-FrameWork.html.

  • Hirashima, T., Hachiya, K., Kashihara, A. and Toyoda, J.: 1997, Information filtering using user's context on browsing in hypertext. User Modeling and User-Adapted Interaction 7(4), 239–256.

    Google Scholar 

  • Hoashi, K., Matsumoto, K., Inoue, N. and Hashimoto, K.: 1999, Experiments on the TREC-8 filtering track. Proceedings Eight Text Retrieval Conference (TREC-8).

  • Hofferer, M., Knaus, B. and Winiwarter, W.: 1994, Adaptive information extraction from online messages. Proceedings of Intelligent Multimedia Information Retrieval Systems and Management. RIAO 94, 314–327.

  • Hull, D. A.: 1998, The TREC-6 filtering track: Description and analysis. The 6th Text Retrieval Conference (TREC-6), NIST SP 5–240, 45–68

  • Hull, D. A.: 1999, The TREC-7 filtering track: Description and analysis. The 7th Text Retrieval Conference (TREC-7), NIST SP 5–242, 3–56.

  • Hull, D. A. and Robertson, S.: 2000, The TREC-8 filtering track: Final report. The 8th Text Retrieval Conference (TREC-7).

  • Jennings, A. and Higuchi, H.: 1992, A personal news service based on a user model neural network. IEICE Transactions on Information and Systems E75-D(2), 198–210.

    Google Scholar 

  • Jennings, A. and Higuchi, H: 1993, A user model neural network for a personal news service. User Modeling and User-Adapted Interaction 3(1), 1–25.

    Google Scholar 

  • Kantor, P., Boros, E., Melamed, B. and Melkov, V.: 1999, The information quest: A dynamic model of user's information needs. Proceedings of ASIS 99, 536–545.

  • Kantor, P. B., Boros, E., Melamed, B., Menkov, V., Shapira, B. and Neu, D. J.: 2000, Capturing human intelligence in the net. Communications of the ACM 43(8), 112–115.

    Google Scholar 

  • Kass, R. and Finin, T.: 1989, The role of user models in cooperative interactive systems. Intl. Journal of Intelligent Systems 4, 81–112.

    Google Scholar 

  • Kautz, H., Bart, S. and Shah, M.: 1997: Combining social networks and collaborative filtering. Communications of the ACM 40(3), 63–65.

    Google Scholar 

  • Kay, J.: 1990, Um: a user modeling toolkit. Second Int'l User Modeling Workshop, 11–51.

  • Kay, J. and Kummerfeld, R.: 1995, Customization and delivery of multimedia information. Technical Report, Basser Dept. of Computer Science, University of Sydney.

  • Killander, F., Palgren, O. and Pargman, D.: 1994. GHOSTS — design and system description. http://www.dsv.su.se/~fk/if Doc/Ghostdoc.html.

  • Kim, Y., Hahn, S. and Zhang, B.: 2000, Text filtering by boosting naive bayes classifiers. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development Information Retrieval, July 24–28, 2000, Athens Greece, 168–175.

  • Konstan, A., Bradley, N. M., Maltz, D., Herlocker, J. L., Gordon, L. R. and Riedl, J.: 1997, Group Lens:., Applying collaborative filtering to usenet news. Communications of the ACM 40(3), 77–87.

    Google Scholar 

  • Korfhage, R.: 1997, Information Storage and Retrieval, John Wiley and Sons, Inc.

  • Kraft, D. H. and Bookstein, A.: 1978, Evaluation of information retrieval systems: A decision theory approach. Journal of the American Society for Information Science 29(1), 31–40.

    Google Scholar 

  • Kuflik, T. and Shoval, P.: 2000, Generation of user profiles for information filtering — research agenda. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development Information Retrieval, July 24–28, 2000, Athens Greece, 313–315.

  • Lang, K.: 1995, NewsWeeder: Learning the filter netnews. Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, CA, 331–339.

  • Lanquillon, C. and Renz, I.: 1999, Adaptive information filtering: Detecting changes in text streams. Proceedings of the 8th International Conference on Information Knowledge Management, November, 1999, Kansas City, MO, USA, 538–544.

  • Losee, R. M.: 1995, Determining information retrieval and filtering performance without experimentation. Information Processing and Management 31(4), 555–572.

    Google Scholar 

  • Losee, R. M.: 2000, When information retrieval measures agree about the relative quality of document rankings. Journal of the American Society for Information Science 51(9), 834–840.

    Google Scholar 

  • Maes, P. and Kozierok, R.: 1993, Learning interface agents. Proceedings of AAAI, 459–465.

  • Malone, T., Grant, K., Turbak, F., Brobst, S. and Cohen, M.: 1987, Intelligent information sharing systems. Communications of the ACM 30(5), 390–402.

    Google Scholar 

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

    Google Scholar 

  • Marinilli, M., Micarelli, A. and Sciarrone, F.: 1999, A case-based approach to adaptive information filtering for the WWW. Second Workshop on Adaptive Systems and User Modeling on the World Wide Web, 7th International Conference on User Modeling, Banff, Canada, June 20–24.

  • McCalla, G., Searwar, F., Thomson, J., Collins, J., Sun, Y. and Zhou, B.: 1996, Analogical user modeling: A case study in individualized information filtering. Proceedings of the 5th International Conference on User Modeling, 13–20.

  • McCleary, H.: 1994, Filtered information services — A revolutionary new product or a new marketing strategy? Online 4(18), 35–42.

    Google Scholar 

  • Metz, C.: 2000, Personalization: The tailor-made web. PC Magazine 19, 59–162. http://www. zdnet.com/pcmag/stories/reviews/0,6755,2586434,00.html.

    Google Scholar 

  • Microsoft: 2000, http://www.microsoft.com.

  • Michel, C.: 2000, Diagnostic evaluation of a personalized filtering information retrieval, system. Methodology and experimental results. RIAO'2000 Conference Proceedings, Vol. 2, April 12–14, 2000, Paris, France, 1578–1589.

    Google Scholar 

  • Mock, K. J. and Vemuri Rao, V.: 1997, Information filtering via hill climbing, WordNet, and index patterns. Information Processing and Management 33(5), 633–644.

    Google Scholar 

  • Mooney, R. L. and Roy, L.: 2000, Content-based book recommending, using learning for text categorization. Proceedings of the 5th ACM Conference on Digital Libraries, June 2–7, 2000, San Antonio, Texas, USA, 195–204.

  • Morita, M. and Shinoda, Y.: 1994, Information filtering based on user behavior analysis and best match retrieval. Proceedings of the 17th Annual Intl. ACM SIGIR Conference on Research and Development, 272–281.

  • Mostafa, J., Mukhopadhyay, S., Lam, W. and Palakal, M.: 1997, A multilevel approach to intelligent information filtering: Model, system and evaluation. ACM Transaction on Information Systems 15(4), 368–399.

    Google Scholar 

  • Moukas, A.: 1996, Amalthaea: Information discovery and filtering using a multiagent evolving ecosystem. Proceedings of the Conference on Practical Applications of Agents and Multiagent Technology, London, April 96.

  • Moukas, A. and Zacharia, G.: 1997, Evolving a multi-agent information filtering, solution in Amalthaea. Proceedings of the First International Conference on Autonomous Agents, February 5–8, 1997, Marina del Rey, CA, USA, 394–403.

  • Moura, A. M. C., Campos, M. L. M. and Barreto, C. M.: 1998, A survey on metadata for describing and retrieving internet resources. World Wide Web 1, 221–228.

    Google Scholar 

  • Netscape: 2000, http://www.netscape.com.

  • Newell, S. C.: 1997, User models and filtering agents for improved internet information retrieval. User Modeling and User-Adapted Interaction 7(4), 223–237.

    Google Scholar 

  • Oard, W. D.: 1997, The state of the art in text filtering, User Modeling and User Adapted Interaction (UMUAI) 7(3), 141–178.

    Google Scholar 

  • Oard, W. D.: 1998, Implicit Feedback for Recommender Systems. Proceedings of the American Association for Artificial Intelligence Workshop of Collaborative Systems (AAAI98), 80–83.

  • Oard, W. D. and Jimook, K.: 2000, Information Filtering resources. http://www.clis.umd.edu/dlrg/filter/.

  • Olsson, T.: 1998, Decentralized social filtering based on trust. Recommender Systems Workshop Papers, Technical Report WS–98–08, AAAI Press, 84–88.

  • Orfali, R., Harkey, D. and Edwards, J.: 1996, The Essential Client/Server Survival Guide, 2nd Ed., New York: John Wiley & Sons.

    Google Scholar 

  • Pazzani, M. L. and Billsus, D.: 1999, Adaptive Web site agents. Proceedings of the Third Annual Conference on Autonomous Agents, May 1–5, 1999, Seattle, WA, USA, 394–395.

  • Pinar, A. and Cetintemel, U.: 1995, Wide-area distributed selective dissemination of information. Proceedings of ISCIS X — The 10th Intl. Symposium on Computer and Information Sciences, 281–288.

  • Procter, R. and McKinlay, A.: 1997, Lightweight collaborations for social filtering on the web. Position Paper for 5th DELOS Workshop on Collaborative Filtering, Budapest, November 97.

  • Quiroga, L. M. and Mostafa, J.: 1990, Empirical evaluation of explicit versus implicit acquisition of user profiles in information filtering systems. Proceedings of the Fourth ACM Conference on Digital Libraries, August 11–14, 1999, Berkeley, CA, USA, 238–239.

  • Raskutti, B., Beitz, A. and Ward, B.: 1997, A feature-based approach to recommending selections based on past preferences. User Modeling and User-Adapted Interaction 7(3), 179–218.

    Google Scholar 

  • Resnick, P. and Varian, H. R.: 1997, Recommender systems. Communications of the ACM 40(3), 56–58.

    Google Scholar 

  • Rich, E.: 1989, Stereotypes and user modeling. In: A. Kobsa and W. Wahster (eds): User Models in Dialog Systems, Springer Verlag, 35–51.

  • Rhodes, B. J.: 1997, The wearable remembrance agent: a system for augmented memory. Personal Technologies Journal Special Issue on Wearable Computing, Personal Technologies 1, 218–224.

    Google Scholar 

  • Rhodes, B. J. and Starner, T.: 1996, Remembrance agent, a continuously running automated information retrieval system. The Proceedings of The First International Conference, on The Practical Application Of Intelligent Agents and Multi Agent Technology (PAAM '96), 487–495.

  • Robert-Witt, S.: 2000, Return to sender: Filtering. PC Magazine, May 9, 2000, 19(9), 190–197.

    Google Scholar 

  • Robertson, S. E. and Sparck-Jones, K.: 1976, Relevance weighting of search terms. Journal of ASIS 27(3), 129–146.

    Google Scholar 

  • Rocchio, J. J.: 1971, Performance indices for document retrieval. In: G. Salton (ed.): The SMART Retrieval System — Experiments in Automatic Document Processing, Englewood, NJ, 57–67.

  • Rust, G.: 1998, Metadata: The right approach an integrated model for descriptive and rights metadata in e-commerce. D-Lib Magazine, July/August 1998, ISSN 1082–9873. http://www.dlib.org/dlib/july98/rust/07rust.html.

  • Ryan, M. E. and Triverio, J.: 1999, Internet filtering: Net guards. PC Magazine, May 4, 1999, 8(9), 273–278.

    Google Scholar 

  • Salton, G.: 1989, Automatic Text Processing. Reading, Massachusetts: Addison-Wesley.

    Google Scholar 

  • Salton, G. and McGill, W. J.: 1983, Introduction to Modern Information Retrieval. New York: McGraw-Hill.

    Google Scholar 

  • Sarwar, B. M, Konstan, J. A., Borehers, A., Herlocken, J., Miller, B and Reitl, J.: 1998, Using filtering Agents to improve prediction quality in the GroupLens research collaborative filtering system. Proceedings the ACM 1998 Conference on Computer Supported Cooperative Work, November 14–18, 1998, Seattle, WA, USA, 345–354.

  • Seo, Y. and Zhang, B.: 2000, A reinforcement learning agent for personalized information filtering. Proceedings of the 2000 International Conference on Intelligent User Interfaces, January 9–12, 2000, New Orleans, LA, USA, 248–251.

  • Shardanand, U. and Maes, P.: 1995, Social information filtering algorithms for automating ‘Word of Mouth’. Proceedings of the 1995 ACM Conference on Human Factors in Computing Systems, 210–217.

  • Sheth, B.: 1994, A learning approach to personalized information filtering. Master's Thesis, Massachusetts Institute of Technology.

  • Shapira, B., Hanani, U., Raveh, A. and Shoval, P.: 1997, Information filtering: A new two-phase model using stereotypic profiling. Journal of Intelligent Information Systems 8, 155–165.

    Google Scholar 

  • Shapira, B., Shoval, P. and Hanani, U.: 1997, Stereotypes in information filtering systems. Information Processing and Management 33(3), 273–287.

    Google Scholar 

  • Shapira, B., Shoval, P. and Hanani, U.: 1999. Experimentation with an Information filtering, system that combines cognitive and sociological filtering integrated with user stereotypes. Decision Support Systems 27(1999), 5–24.

    Google Scholar 

  • Sharon, T. and Frank, A. J.: 2000, Digital libraries on the internet, 66th IFLA Council and General Conference Jerusalem, Israel, 13–18 August. http://www.ifla.org/IV/ifla66/papers/029–142e.htm.

  • Smyth, B. and Cotter, P.: 2000, A personalized television listings service. Communications of the ACM 43(8), 107–111.

    Google Scholar 

  • Stadnyk, I. and Kass, R.: 1992, Modeling users' interests in information filters. Communications of the ACM 35(12), 49–50.

    Google Scholar 

  • Stanek, W. R.: 1997, Pushing the envelope with push technology. PC Magazine Online 16(16).

  • Stefani, A. and Strapparava, C.: 1999, Exploiting NLP techniques to build user model for Web sites: the use of WordNet in SiteIF Project. Second Workshop on Adaptive Systems and User Modeling on the World Wide Web, 8th International World Wide Web Conference, Toronto, Canada, May 11–14, 1999.

  • Su, L. T.: 1991, An investigation to find appropriate measures for information retrieval. Ph.D. thesis. Rutgers, State University of New Jersey, USA.

    Google Scholar 

  • Tague-Sutcliffe, J.: 1992, The pragmatics of information retrieval experimentation, revisited. Information Processing and Management 28(4), 467–490.

    Google Scholar 

  • Tatemura, J.: 1999, Visual querying and explanation of recommendations from collaborative filtering systems. Proceedings of the 1999 International Conference on Intelligent User Interfaces, January 5–8, 1999, Redondo Beach, CA, USA, 189.

  • Tatemura, L.: 2000, Virtual reviewers for collaborative exploration of movie reviews. Proceedings of the 2000 International Conference on Intelligent User Interfaces, pp. 272–275.

  • Terry, D. B.: 1993, A Tour Through Tapestry, Xerox Palo Alto Research Center.

  • Terveen, L., Hill, W., Amento, B., Mcdonald, D. and Creter, J.: 1997, PHOAKS: A system for sharing recommendations. Communications of the ACM 40(3), 59–62.

    Google Scholar 

  • Tjoa, A. M., Hofferer, M., Ehrentraut, G. and Untersmeyer, P.: 1997, Applying Evolutionary Algorithms to the Problem of Information Filtering 8th International Workshop on Database and Expert Systems Applications (DEXA '97), September 1–2, 1997, Toulouse, France.

  • Thomas, C. and Fisher, G.: 1996, Using agents to improve the usability an usefulness of the WWW. Proceedings of the 5th International Conference on User Modeling, 5–12.

  • Turnbull, D.: 1997, KMDI final summary, collaborative filtering, http://donturn.fis.utoronto.ca/research/kmdi-cf.html.

  • Turnbull, D.: 2000, Augmenting Information Seeking on the World Wide Web Using Collaborative Filtering Techniques, University of Toronto, Canada, http://donturn.fis.utoronto.ca/research/augmentis.html.

    Google Scholar 

  • Van Rijsbergen, C. J.: 1979, Information Retrieval. London: Butterworths.

    Google Scholar 

  • Volokh, E.: 2000, Personalization and privacy. Communications of the ACM 3(8), 84–88.

    Google Scholar 

  • W3C, Resource Description Framework (RDF): 2000, http://www.w3.org/RDF/.

  • Waern, A., Tierney, M., Rudsström, Å and Laaksolahti, J.: 1999a, ConCall: edited and adaptive information filtering. Proceedings of the 1999 International Conference on Intelligent User Interfaces, January 5–8, 1999, Redondo Beach, CA USA, p. 185.

  • Waern, A., Averman, C., Tierney, M. and Rudsström, Å: 1999b, Information services based on user profile communication. In: Judy Kay (ed.): International Conference on User Modeling, UM'99 User Modeling: Proceedings of the Seventh International Conference UM99, New York, Springer Wien.

    Google Scholar 

  • Wolinski, F., Vichot, F. and Stricker, M.: 2000, Using learning-based filters detect rule-based filtering obsolescence. RIAO'2000 Conference Proceedings, Vol. 2, April 12–14, 2000, Paris, France, 1208–1220.

    Google Scholar 

  • WordNet: 2000, a Lexical Database for English, http://www.cogsci.princeton.edu/~wn/.

  • Wu, Y. and Chen, A. L. P.: 2000, Index structures of user profiles for efficient web page filtering services. The 20th International Conference on Distributed Computing Systems (ICDCS 2000), April 10–13 2000, Taipei, Taiwan.

  • Yan, T. W. and Garcia-Molina, H.: 1994, Stanford information filtering tool. Computer Science Technical Report, Stanford University.

  • Yahoo: 2000, http://www.yahoo.com.

  • My Yahoo: 2000, http://my.yahoo.com.

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems Engineering, Ben-Gurion University, Beer-Sheva, 841055, Israel

    Uri Hanani, Bracha Shapira & Peretz Shoval

Authors
  1. Uri Hanani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bracha Shapira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peretz Shoval
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peretz Shoval.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hanani, U., Shapira, B. & Shoval, P. Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001). https://doi.org/10.1023/A:1011196000674

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1011196000674

Search

Navigation