article

Text-Learning and Related Intelligent Agents: A Survey

Author:

Dunja MladenicAuthors Info & Claims

IEEE Intelligent Systems, Volume 14, Issue 4

Pages 44 - 54

https://doi.org/10.1109/5254.784084

Published: 01 July 1999 Publication History

Abstract

Analysis of text data using intelligent information retrieval, machine learning, natural language processing or other related methods is becoming an important issue for the development of intelligent agents. There are two frequently used approaches to the development of intelligent agents using machine learning techniques: a content-based and a collaborative approach. In the first approach, the content (eg., text) plays an important role, while in the second approach, the existence of several knowledge sources (eg., several users) is required. We can say that the usage of machine learning techniques on text databases (usually referred to as text-learning) is an important part of the content-based approach. Examples are agents for locating information on World Wide Web and Usenet news filtering agents. There are different research questions important for the development of text-learning intelligent agents. We focus on three of them: what representation is used for documents, how is the high number of features dealt with and which learning algorithm is used. These questions are addressed in an overview of the existing approaches to text classification. For illustration we give a brief description of the content-based personal intelligent agent named Personal WebWatcher that uses text-learning for user customized Web browsing.

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Text-Learning and Related Intelligent Agents: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Information systems

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cover image IEEE Intelligent Systems

IEEE Intelligent Systems Volume 14, Issue 4

July 1999

93 pages

ISSN:1541-1672

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Copyright © Copyright © 1999 IEEE. All Rights Reserved.

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IEEE Educational Activities Department

United States

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Published: 01 July 1999

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Cited By

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https://dl.acm.org/doi/10.1016/j.jksuci.2019.06.008
Nguyen NPham VDang T(2021)HMaViz: Human-machine analytics for visual recommendationProceedings of the 12th International Conference on Advances in Information Technology10.1145/3468784.3471601(1-9)Online publication date: 29-Jun-2021
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Jubran ITukan MMaalouf AFeldman DDaumé HSingh A(2020)Sets clusteringProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525402(4994-5005)Online publication date: 13-Jul-2020
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El-Ansari ABeni-Hssane ASaadi MBen Ahmed MBoudhir A(2020)An improved modeling method for profile-based personalized searchProceedings of the 3rd International Conference on Networking, Information Systems & Security10.1145/3386723.3387874(1-6)Online publication date: 31-Mar-2020
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Intayoad WKamyod CTemdee P(2020)Reinforcement Learning Based on Contextual Bandits for Personalized Online Learning Recommendation SystemsWireless Personal Communications: An International Journal10.1007/s11277-020-07199-0115:4(2917-2932)Online publication date: 1-Dec-2020
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Cella LCereda SQuadrana MCremonesi PBielikova MHerder ECena FDesmarais M(2017)Deriving Item Features Relevance from Past User InteractionsProceedings of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3079628.3079695(275-279)Online publication date: 9-Jul-2017
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Kunaver MPorl T(2017)Diversity in recommender systems A surveyKnowledge-Based Systems10.1016/j.knosys.2017.02.009123:C(154-162)Online publication date: 1-May-2017
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Rodrigues Mda Silva GDurão Fde Jesus Lima Gomes Fde Andrade Lira Rabelo Rde Salles Soares Neto CWillrich RCamillo Teixeira Cde Almeida Jde Carvalho W(2016)User Models Development Based on Cross-Domain for Recommender SystemsProceedings of the 22nd Brazilian Symposium on Multimedia and the Web10.1145/2976796.2988211(363-366)Online publication date: 8-Nov-2016
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