Abstract
In this paper we present a novel news filtering model based on flexible and soft filtering criteria and exploiting a fuzzy hierarchical categorization of news. The filtering module is designed to provide news professionals and general users with an interactive and personalised tool for news gathering and delivery. It exploits content-based filtering criteria and category-based filtering techniques to deliver to the user a ranked list of either news or clusters of news. In fact, if the user prefers to have a synthetic view of the topics of recent news pushed by the stream, the system filters groups (clusters) of news having homogenous contents, identified automatically by the application of a fuzzy clustering algorithm that organizes the recent news into a fuzzy hierarchy. The filter can be trained explicitly by the user to learn his/her interests as well as implicitly by monitoring his/her interaction with the system. Several filtering criteria can be applied to select and rank news to the users based on the user’s information preferences and presentation preferences. User preferences specify what information (the contents of interest) is relevant to the user, the sources that provide reliable information, and the period of time during which the information remains relevant. Each individual news or cluster of news homogeneous with respect to their content is selected based on a customizable multi criteria decision making approach and ranked based on a combination of criteria specified by the user in his/her presentation preferences.
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References
Amato, G., Straccia, U., Thanos, C.: EUROgatherer: a Personalised Gathering and Delivery Service on the Web. In: Proc. of the 4th SCI 2000 (2000)
Belkin, N.J., Croft, W.B.: Information filtering and Information Retrieval: Two sides of the same Coin? Communications of the ACM 35(12) (1992)
Bell, T.A.H., Moffat, A.: The Design of a High Performance Information Filtering System. In: SIGIR 1996, Zurich, Switzerland (1996)
Bordogna, G., Pagani, M., Pasi, G., Antoniolli, L., Invernizzi, F.: An Incremental Hierarchical Fuzzy Clustering Algorithm Supporting News Filtering. In: Proc. IPMU 2006, Paris (2006)
Bordogna, G., Pasi, G., Pagani, M., Villa, R.: PENG Filtering model and Demos. PENG Deliverable 3.1 (November 2005)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-based and Collaborative Filters in an Online Newspaper. In: Proc. ACM SIGIR 1999 Workshop on Recommender Systems-Implementation and Evaluation (1999)
Connor, M., Herlocker, J.: Clustering for Collaborative Filtering. In: Proc. of ACM SIGIR Workshop on Recommender Systems (1999)
Crestani, F., Pasi, G. (eds.): Soft Computing in Information Retrieval: Techniques and Applications. Physica-Verlag, Heidelberg (2000)
Foltz, P.W., Dumais, S.T.: Personalized information delivery: an analysis of information filtering methods. Communications of the ACM 35(12), 29–38 (1992)
Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: Statistical semantics: An analysis of the potential performance of keyword information systems. Bell Syst. Tech. J. 62(6), 1753–1806 (1983)
Gabrilovich, S.D., Horvitz, E.: Newsjunkie: Providing Personalized Newsfeeds via Analysis of Information Novelty. In: WWW 2004, New York (2004)
Hathaway, R.J., Bezdek, J.C., Hu, Y.: Generalized Fuzzy C-Means Clustering Strategies Using Lp Norm Distances. IEEE Trans. on Fuzzy Systems 8(5), 576–582 (2000)
Kilander F.: A brief comparison of News filtering Software, http://www.glue.umd.edu/enee/medlab/filter/filter.html
Kraft, D., Chen, J., Martin–Bautista, M.J., Vila, M.A.: Textual Information Retrieval with User Profiles using Fuzzy Clustering and Inferencing. In: Szczepaniak, P., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.) Intelligent Exploration of the Web. Studies in Fuzziness and Soft Comp. Series, vol. 111, Physica Verlag, Heidelberg (2003)
Mackay, W.E., Malone, T.W., Crowston, K., Rao, R., Rosenblitt, D., Card, S.K.: How do experienced information lens user use rules? In: Proceedings of ACM CHI 1989 Conference on Human Factors in Computing Systems, Austin, Tex., April 30-May 4, pp. 211–216. ACM/SIGCHI, New York (1989)
Miyamoto, S.: Fuzzy IR and clustering techniques. Kluwer, Dordrecht (1990)
Oard, D.W., Marchionini, G.: 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 (1996)
Pasi, G., Villa, R.: The PENG Project overview. In: IDDI-05-DEXA Workshop, Copenhagen (2005)
Robertson, S.E., Walker, S.: Okapi/Keenbow at TREC-8., In NIST Special Publication 500-246. The Eighth Text REtrieval Conference (TREC 8) (1999)
Salton, G., McGill, M.J.: Introduction to modern information retrieval. McGraw-Hill, New York (1984)
Sollenborn, M., Funk, P.: Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS, vol. 2416, p. 395. Springer, Heidelberg (2002)
Ungar, L.H., Foster, D.P.: Clustering Methods for Collaborative Filtering. In: Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park (1998)
Wong, W.-c., Fu, A.W.-c.: Incremental Document Clustering for Web Page Classification. In: Proc. Int. Conf. IS 2000, Aizu-Wakamatsu City, Japan (2000)
Yan, T.W., Garcia-Molina, H.: Index Structures for Information Filtering Under the Vector Space Model. In: Proc. 10th IEEE Int. Conf. on Data Engineering, Houston, pp. 337–347 (1994)
Zhang, Y., Callan, J., Minka, T.: Novelty and Redundancy Detection in Adaptive Filtering. In: Proc. of SIGIR 2002, Tampere, Finland (2002)
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Bordogna, G., Pagani, M., Pasi, G., Villa, R. (2006). A Flexible News Filtering Model Exploiting a Hierarchical Fuzzy Categorization. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_15
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DOI: https://doi.org/10.1007/11766254_15
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