Dynamic models of expert groups to recommend web documents

Dae Eun Kim; Sea Woo Kim

doi:10.1007/3-540-44796-2_24

Dynamic models of expert groups to recommend web documents

Dae Eun Kim, Sea Woo Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Recently most recommender systems have been developed to recommend items or documents based on user preferences for a particular user, but they have difficulty in deriving user preferences for users who have not rated many documents. In this paper we use dynamic expert groups which are automatically formed to recommend domainspecific documents for unspecified users. The group members have dynamic authority weights depending on their performance of the ranking evaluations. Human evaluations over web pages are very effective to find relevant information in a specific domain. In addition, we have tested several effectiveness measures on rank order to determine if the current top-ranked lists recommended by experts are reliable. We show simulation results to check the possibility of dynamic expert group models for recommender systems.

Original language	English
Title of host publication	Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings
Editors	Panos Constantopoulos, Panos Constantopoulos, Ingeborg T. Sølvberg
Publisher	Springer Verlag
Pages	275-286
Number of pages	12
ISBN (Electronic)	9783540425373
DOIs	https://doi.org/10.1007/3-540-44796-2_24
Publication status	Published - 2001
Event	5th European Conference on Research and Advanced Technology for Digital Libraries, ECDL 2001 - Darmstadt, Germany Duration: 2001 Sep 4 → 2001 Sep 9

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2163
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	5th European Conference on Research and Advanced Technology for Digital Libraries, ECDL 2001
Country	Germany
City	Darmstadt
Period	01/9/4 → 01/9/9

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/3-540-44796-2_24

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Kim, D. E., & Kim, S. W. (2001). Dynamic models of expert groups to recommend web documents. In P. Constantopoulos, P. Constantopoulos, & I. T. Sølvberg (Eds.), Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings (pp. 275-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2163). Springer Verlag. https://doi.org/10.1007/3-540-44796-2_24

Kim, Dae Eun ; Kim, Sea Woo. / Dynamic models of expert groups to recommend web documents. Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings. editor / Panos Constantopoulos ; Panos Constantopoulos ; Ingeborg T. Sølvberg. Springer Verlag, 2001. pp. 275-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Dynamic models of expert groups to recommend web documents",

abstract = "Recently most recommender systems have been developed to recommend items or documents based on user preferences for a particular user, but they have difficulty in deriving user preferences for users who have not rated many documents. In this paper we use dynamic expert groups which are automatically formed to recommend domainspecific documents for unspecified users. The group members have dynamic authority weights depending on their performance of the ranking evaluations. Human evaluations over web pages are very effective to find relevant information in a specific domain. In addition, we have tested several effectiveness measures on rank order to determine if the current top-ranked lists recommended by experts are reliable. We show simulation results to check the possibility of dynamic expert group models for recommender systems.",

author = "Kim, {Dae Eun} and Kim, {Sea Woo}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 5th European Conference on Research and Advanced Technology for Digital Libraries, ECDL 2001 ; Conference date: 04-09-2001 Through 09-09-2001",

year = "2001",

doi = "10.1007/3-540-44796-2_24",

language = "English",

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Kim, DE & Kim, SW 2001, Dynamic models of expert groups to recommend web documents. in P Constantopoulos, P Constantopoulos & IT Sølvberg (eds), Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2163, Springer Verlag, pp. 275-286, 5th European Conference on Research and Advanced Technology for Digital Libraries, ECDL 2001, Darmstadt, Germany, 01/9/4. https://doi.org/10.1007/3-540-44796-2_24

Dynamic models of expert groups to recommend web documents. / Kim, Dae Eun; Kim, Sea Woo.

Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings. ed. / Panos Constantopoulos; Panos Constantopoulos; Ingeborg T. Sølvberg. Springer Verlag, 2001. p. 275-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2163).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Dynamic models of expert groups to recommend web documents

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N2 - Recently most recommender systems have been developed to recommend items or documents based on user preferences for a particular user, but they have difficulty in deriving user preferences for users who have not rated many documents. In this paper we use dynamic expert groups which are automatically formed to recommend domainspecific documents for unspecified users. The group members have dynamic authority weights depending on their performance of the ranking evaluations. Human evaluations over web pages are very effective to find relevant information in a specific domain. In addition, we have tested several effectiveness measures on rank order to determine if the current top-ranked lists recommended by experts are reliable. We show simulation results to check the possibility of dynamic expert group models for recommender systems.

AB - Recently most recommender systems have been developed to recommend items or documents based on user preferences for a particular user, but they have difficulty in deriving user preferences for users who have not rated many documents. In this paper we use dynamic expert groups which are automatically formed to recommend domainspecific documents for unspecified users. The group members have dynamic authority weights depending on their performance of the ranking evaluations. Human evaluations over web pages are very effective to find relevant information in a specific domain. In addition, we have tested several effectiveness measures on rank order to determine if the current top-ranked lists recommended by experts are reliable. We show simulation results to check the possibility of dynamic expert group models for recommender systems.

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M3 - Conference contribution

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings

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Kim DE, Kim SW. Dynamic models of expert groups to recommend web documents. In Constantopoulos P, Constantopoulos P, Sølvberg IT, editors, Research and Advanced Technology for Digital Libraries - 5th European Conference, ECDL 2001, Proceedings. Springer Verlag. 2001. p. 275-286. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-44796-2_24

Dynamic models of expert groups to recommend web documents

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