Adaptive Information Filtering as a Means to Overcome Information Overload

Information Filtering is concerned with filtering data streams in such a way as to leave only pertinent data (information) to be perused. When the data streams are produced in a changing environment (as most if not all are) the filtering has to adapt too in order to remain effective. Adaptive Information Filtering (AIF) is concerned with filtering in changing environments. The changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). The thesis research described in this paper combines trigram analysis, clustering, and evolutionary computation, in an effort to create an AIF system with such useful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly.

[1]  Bernd Teufel,et al.  Full text retrieval based on syntactic similarities , 1988, Inf. Syst..

[2]  E. Backer,et al.  Computer-assisted reasoning in cluster analysis , 1995 .

[3]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (2nd, extended ed.) , 1994 .

[4]  T. de Heer.  The application of the concept of homeosemy to natural language information retrieval , 1982, Inf. Process. Manag..

[5]  Julie B. Lovins.  Error evaluation for stemming algorithms as clustering algorithms , 1971 .

[6]  William S. Cooper.  The formalism of probability theory in IR: a foundation or an encumbrance? , 1994, SIGIR '94.

[7]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[8]  Nicholas J. Belkin,et al.  Information filtering and information retrieval: two sides of the same coin? , 1992, CACM.

[9]  Daniel R. Tauritz,et al.  Adaptive Resonance Theory (ART): An Introduction , 1995 .

[10]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[11]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[12]  David Dubin.  Measurement in information science , 1997 .

[13]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[14]  Daniel R. Tauritz.  Concepts of Adaptive Information Filtering , 1996 .

[15]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[16]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[17]  Guszti Bartfai,et al.  An ART-based modular architecture for learning hierarchical clusterings , 1996, Neurocomputing.

[18]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[19]  Beerud Dilip Sheth,et al.  A learning approach to personalized information filtering , 1994 .

[20]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[21]  Gerard Salton,et al.  Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .

[22]  B. Moore,et al.  ART1 and pattern clustering , 1989 .

[23]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[24]  Bjarne Stroustrup,et al.  C++ Programming Language , 1986, IEEE Softw..

[25]  Chris D. Paice,et al.  Another stemmer , 1990, SIGF.

[26]  Peter Willett,et al.  An evaluation of some conflation algorithms for information retrieval , 1981 .

[27]  Glynn Harmon,et al.  The measurement of information , 1984, Inf. Process. Manag..

[28]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[29]  Julie Beth Lovins,et al.  Development of a stemming algorithm , 1968, Mech. Transl. Comput. Linguistics.

[30]  Daniel R. Tauritz.  Optimization of the Discriminatory Power of a Trigram Based Document Clustering Algorithm Using Evolutionary Computation , 1996 .

[31]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[32]  Pattie Maes,et al.  Agents that reduce work and information overload , 1994, CACM.

[33]  Timo Honkela,et al.  Newsgroup Exploration with WEBSOM Method and Browsing Interface , 1996 .

[34]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..