Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/25323
Title: | Metasearch + machine learning = WWW - information overload |
Authors: | Montebello, Matthew |
Keywords: | Search engines Artificial intelligence XML (Document markup language) Information retrieval |
Issue Date: | 1999 |
Publisher: | JCI |
Citation: | Montebello, M. (1999). Metasearch + machine learning = WWW - information overload. Journal of Computing and Information (JCI), 3(1). |
Abstract: | AI methodologies and the application of machine learning techniques to optimize services provided by existent internet search technologies is one way to control and manage the immense and ever-increasing volume of data published on the \VW\V. Users demand effective and efficient on-line information access in away to reduce the information overload. In this paper we present a novel approach to achieve these objectives by generating information which is of a high recall quality- by reusing the output generated from major search engines and other previously developed systems; and of a high precision calibre- by generating specific user profiles after several interactions with the system. This paper discusses the techniques involved) as well as practical issues such as information reuse, evolvability1profile generation and graphic user interfaces. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/25323 |
ISSN: | 12018511 |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
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