- Apply for Authority
- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
Machine-Learning Based on Relevance Feedback: A Powerful Engine to Enhance the Performance of SDI System
- Downloaded
- Viewed
Abstract
As the Internet facilitates the rapid increase of information availability, the study on SDI service that provides users with relevant document in a timely manner has been developed. However, the practical use of this service has been low. This thesis aims at analyzing the reasons for this and developing relevance feedback based SDI system to improve the performance of the existing SDI system. Experimental systems that are developed for this study are SDI system based on users' minimum intervention feedback, SDI system based on perfect automation feedback, and SDI system based on users' maximum intervention feedback. The fourth system that utilizes the traditional SDI system is also studied to evaluate the level of performance improvement of the newly developed three types of SDI system. As a result of this study, SDI system based on users' maximum intervention feedback showed greatest performance improvement. The next performance improvement happened in order of SDI system based on perfect automation feedback, SDI system based on users' minimum intervention feedback, and the traditional SDI system. Feedback based systems showed greater performance improvement as they went through more feedback processes.
- keywords
- 기계학습, 적합성 피드백, 최신정보주지, 적합문헌SDI, Machine Learning, Relevance Feedback
Reference
노영희. (2003). 국내 대학도서관의 SDI 서비스 제공현황 분석 및 통합형 서비스 시스템 구축 방안에 관한 연구. 정보관리학회지, 20(3), 199-203.
정영미. (1993). 정보검색론:구미무역.
Amati, Gianni. (1997). Learning System for Selective Dissemination of Information (764-769). 15th International Joint Conference on Artificial Intelligence.
Belkin, N. J. (1992). “Information Filtering and Information Retrieval: Two Sides of the Same Coi. Communications of the ACM,, 35(12), 29-38.
Blake, P. (1997). Exploring the News:Information World Review.
Bonifati, Angela. (2001). Pushing Reactive Services to XML Repositories using Active Rules. 39(5), 633-641.
Boughanem, M. (2002). Incremental Adaptive Filtering: Profile Learning and Threshold Calibration.. ACM, , 640-644.
Callan, J. P. (1995). TREC and TIPSTER Experiments with INQUERY. Information Processing and Management, 31(3), 327-343.
Callan, Jamie.. (1119). Document Filtering with Inference Networks. SIGIR, 96, 262-269.
Chung, Young Mee. (2003). A Study on Automatic text categorization of internet document. Journal of Information Science, 29(1), 117-126.
Dasarathy, Belur V. (1991). Nearest Neighbor(NN) Norms: NN Patern Classification Techniuqes. McGraw- Hill Computer Science Series. Las Alamitos,:IEEE Computer Society Press..
Fischer, G. (1991). Information Access in Complies, Poorly Structured Information Spaces (63-70). ACM Special Interest Group on Human Computer Interaction Annual Conference.
Foltz, P. W. (1992). Personalized Information Delivery: An Analysis of Information Filtering Methods. Communications of the ACM, 35(12), 51-60.
Frants, V. I. One Approach to Classification of Users and Automatic Clustering of Documents. Information Processing and Management, 29(2), 187-195.
Goker, A. (1991). Toward an Adaptive Information Retrieval System (348-357). 6th International Symposium.
Iwayama, Makato. (1995). Cluster-based Text Categorization: A Comparison of Category Search Strategies (273-281). 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).
Lang, K. (1995). NewsWeeder: An adaptive multi-user text filter:Tech. Rep., School of Computer Science.
Manlone, T. W. (1987). Intelligent Information Sharing Systems. Com- mun. ACM, 30(5), 390-402.
Masand, B., G. (1992). “Classifying News Stories Using Memory based Reseonin.
Rich, E. (1983). Users are Individuals: Individualizing User Models. International Journal. Man-Mach. Studies, 18, 199-214.
Robertson, S. E., S. (1995). Walker, S. Jones, M. M. Hancock-Beaulieu, and M. Gatford. Okapi at TREC-3 (500-225). The Third Text Retrieval Conference (TREC- 3).
Salton, Gerald. (1983). Introduction to Modern Information:Retrieval. McGraw-Hill.
Seth, B. D. A Learning Approach to Personalized Information Filtering..
Singhal, Amit. (1997). “Learning Routing Queries in a Query Zone. SIGIR, 97, 25-32.
Walker, S. (1997). :Kkapi/Keebow ant TREC-6 automatic and ad hoc, VLC, Routing, Filtering and QSDR.
Wyle, M. F. Retrieving Highly Dynamic Distributed Information. (108-115). ACM SIGIR lnternational Conference on Research and Development in Information Retrieval:.
Yan T. (1995). SIFT - A tool for wide-area information dissemination (177-186). 1995 USENIX Technical Conference.
Yan T. (1994). Distributed Selective Dissemination of Information (89-98). 3rd International Conference on Parallel and Distributed Information Systems (PDIS, Austin, TX, Sept.).
Yang, Y. Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorization and Retrieval (11-21). 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).
Yochum, J. A. A High-Speed Text Scanning Algorithm Utilizing Least Frequent Trigraph. (114-121). IEEE International Symposium on New Directions in Computing.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations