Abstract
In this paper, a flexible multi criteria information filtering model is presented. This model is flexible since it allows choosing several distinct criteria, such as content aboutness, coverage, novelty, trust, timeliness, and combining them by a soft aggregation to define a personalized filter. The personal filter is encoded into the user profile that also contains the representations of the user interests that can evolve over time. An implementation of the system applying a combination of the aboutness and coverage criteria has been evaluated and compared to other filtering systems, showing its superior effectiveness. Finally, the possible use of the other criterion is discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amato G, Straccia U, Thanos C (2000) EUROgatherer: a personalised gathering and delivery service on the Web. In: Proceedings of the 4th SCI-2000
Belkin, Nicholas J, Bruce CW (1992) Information filtering and information retrieval: two sides of the same coin? Commun ACM 35(12)
Bell TAH, Moffat A (1996) The design of a high performance information filtering system. In: SIGIR’96, Zurich, Switzerland
Foltz PW, Dumais ST (1992) Personalized information delivery: an analysis of information filtering methods. Commun ACM 35(12):29–38
Gabrilovich E, Dumais S, Horvitz E (2004) NewsJunkie: providing personalized newsfeeds via analysis of information novelty. In: Proceedings of the 13th international World Wide Web conference 2004 (WWW 2004), May 2004, New York
Kilander F (2009) A brief comparison of news filtering software. http://www.glue.umd.edu/enee/medlab/filter/filter.html
Lewis DD, Yang Y, Rose T, Li F (2004) RCV1: a new benchmark collection for text categorization research. J Mach Learn Res 5:361–397
Miyamoto S (1990) Fuzzy IR and clustering techniques. Kluwer, Dordrecht
Oard DW, Marchionini G (1996) A Conceptual framework for text filtering, Technical report EE-TR-96-25 CAR-TR-830 CLIS-TR-96-02 CS-TR-3643, University of Maryland
Pasi G (2003) Modelling users’ preferences in systems for information access. Int J Intell Syst 18:793–808
Pasi G, Villa R (2005) The PENG project overview. In: IDDI-05-DEXA Workshop, Copenhagen
Pasi G, Bordogna G, Villa R (2007) A multi-criteria content-based filtering system. In: Proceedings of SIGIR 2007, poster paper, pp 775–776
Robertson S, Soboroff I (2002) The TREC 2002 filtering track report. In: Voorhees EM, Harman DK (eds) The 11th text retrieval conference, TREC 2002, Gaithersburg, MD, NIST, 2002 Special Publication 500-250
Robertson S, Soboroff I (2002) The TREC 2002 filtering track report
Robertson SE, Walker S (1999) Okapi/Keenbow at TREC-8. In: NIST special publication 500-246. The 8th text retrieval conference (TREC 8)
Salton G, McGill MJ (1984) Introduction to modern information retrieval. McGraw-Hill
Yan TW, Garcia-Molina H (1994) Index structures for information filtering under the vector space model. In: Proceedings 10th IEEE international conference on data engineering, Houston, pp 337–347
Zhang Y, Callan J, Minka T (2002) Novelty and redundancy detection in adaptive filtering. In: Proceedings of SIGIR’02, Tampere, Finland
Acknowledgments
PENG “Personalized News content programminG Information” is a Specific Targeted Research Project (IST-004597) funded within the Sixth Program Framework of the European Research Area.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bordogna, G., Pasi, G. A flexible multi criteria information filtering model. Soft Comput 14, 799–809 (2010). https://doi.org/10.1007/s00500-009-0476-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-009-0476-3