[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/IJFIS.2008.8.3.175

Evaluation of User Profile Construction Method by Fuzzy Inference

Kim, Byeong-Man (Kumoh National Institute of Technology)
Rho, Sun-Ok (Kumoh National Institute of Technology)
Oh, Sang-Yeop (Kumoh National Institute of Technology)
Lee, Hyun-Ah (Kumoh National Institute of Technology)
Kim, Jong-Wan (Daegu University)

Publication Information

International Journal of Fuzzy Logic and Intelligent Systems / v.8, no.3, 2008 , pp. 175-184 More about this Journal

Abstract

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.

Keywords

User Modeling; Information Filtering; Classification; Keywords Extraction;

Citations & Related Records

Reference

1	T. Joachims, 'Text Categorization with Support Vector Machines: Learning with Many Relevant Features,' Proc. of European Conference on Machine Learning, pp. 137-142, 1998 DOI ScienceOn
2	Byeong Man Kim, Ju Youn Kim and Jongwan Kim, 'Query Term Expansion and Reweighting using Term Co-Occurrence Similarity and Fuzzy Inference,' Proc. of IFSA/NAFIPS, pp.715-720, 2001
3	K. Lam and C. Ho, 'Using a generalized instance set for automatic textcategorization,' Proc. of 21th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 88-89, 1998
4	Kwok-Yin Lai and Wai Lam, 'Automatic Textual Document Categorization Using Multiple Similarity-Based Models,' Proc. of SDM01, 2001
5	D. M. Nichols, 'Implicit ratings and filtering,' Proc. of the 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 10-12, 1997
6	R. Schapire, Y. Singer and A. Singal, 'Boosting and Rocchio Applied to Text Filtering,' Proc. of 21th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, 1998
7	Y. Yang and X. Liu, 'A re-examination of text categorization methods,' Proc. of 22nd Int. ACM SIGIR Conference on Research and Development in Information Retrieval, 1999
8	J. Kim, D.W. Oard and K. Romanik, 'User modeling for information filtering based on implicit feedback,' Proc. of ISKO-France, 2000
9	Y. Seo and B. Zhang, 'Personalized Web Document Filtering Using Reinforcement Learning,' Applied Artificial Intelligence, 2001
10	D. Goldberg, D. Nichols, B. M. Oki and Douglas Terry, 'Using Collaborative Filtering to Weave an Information Tapestry. Commun,' ACM, 35, 1992
11	T. W. Yan and H. Garcia-Molina, 'SIFT- A tool for wide-area information dissemination,' Proc. of the 1995 USENIX Technical Conference, 1995
12	Y. Yang, 'Expert network: effective and efficient learning from human decisions in text categorization and retrieval,' Proc. of 17th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, 1994
13	lan Ruthven and M. Lalmas, 'A survey on the use of relevance feedback for information access systems,' Knowledge Engineering Review, 18 (2), pp. 95- 145, 2003 DOI ScienceOn
14	D.D. Lewis, R.E. Schapore, J.P. Call and R. Papka, 'Training algorithms for linear text classifiers,' Proc. of 19th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 298-306, 1996
15	Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997
16	http://www.research.att.com/-lewis/reuters21578.html
17	C. Buckley and G. Salton, 'Optimization of relevance feedback weights,' Proc. of 18th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, 1995
18	J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L.R. Gordon and J. Riedl, 'GroupLens: Applying collaborative filtering to Usenet News,' CACM, 40(3), pp. 77-87, 1997 DOI ScienceOn
19	Y. Yang, 'An evaluation of statistical approaches to text,' Journal of Information Retrieval, pp. 67-88, 1999
20	F. Sebastiani, 'Machine Learning in Automated Text,' Technical Report IEI-B4-31-1999, Istituto di Elaborazione dellInformazione, 1999
21	Byeong Man Kim, Qing Li, Kwang-Ho Lee and Bo-Yeong Kang, 'Extraction of Representative Keywords Considering Co-occurrence in Positive Documents,' FSKD 2005 : Fuzzy Systems and Knowledge Discovery, Lipo Wang and Yaochu Jin, Eds., LNAI 3614, Springer-Verlag, pp. 752-761, 2005
22	M. Pazzani and D. Billsus, 'Learning and revising user profiles: the identification of interesting Web sites,' Machine Learning, 1997
23	R. Baeza-Yates andB. Ribeiro-Neto, Modern Information Retrieval, ACM Press, NY, USA, 1999