Probabilistic learning for selective dissemination of information

被引:7
作者
Amati, G
Crestani, F
机构
[1] Fdn Ugo Bordoni, I-00142 Rome, Italy
[2] Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland
关键词
D O I
10.1016/S0306-4573(99)00012-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest, In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of Information Retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile. (C) 1999 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:633 / 654
页数:22
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