Article

An architecture for the aggregation and analysis of scholarly usage data

Authors:

Herbert Van de SompelAuthors Info & Claims

JCDL '06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries

Pages 298 - 307

https://doi.org/10.1145/1141753.1141821

Published: 11 June 2006 Publication History

Abstract

Although recording of usage data is common in scholarly information services, its exploitation for the creation of value-added services remains limited due to concerns regarding, among others, user privacy, data validity, and the lack of accepted standards for the representation, sharing and aggregation of usage data. This paper presents a technical, standards-based architecture for sharing usage information, which we have designed and implemented. In this architecture, OpenURL-compliant linking servers aggregate usage information of a specific user community as it navigates the distributed information environment that it has access to. This usage information is made OAI-PMH harvestable so that usage information exposed by many linking servers can be aggregated to facilitate the creation of value-added services with a reach beyond that of a single community or a single information service. This paper also discusses issues that were encountered when implementing the proposed approach, and it presents preliminary results obtained from analyzing a usage data set containing about 3,500,000 requests aggregated by a federation of linking servers at the California State University system over a 20 month period.

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Cited By

Champiri ZFisher BKiong LDanaee M(2020)How Contextual Data Influences User Experience with Scholarly Recommender Systems: An Empirical FrameworkHCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies10.1007/978-3-030-60114-0_42(635-661)Online publication date: 3-Oct-2020
https://doi.org/10.1007/978-3-030-60114-0_42
Maake BOjo SZuva T(2019)A Survey on Data Mining Techniques in Research Paper Recommender SystemsResearch Data Access and Management in Modern Libraries10.4018/978-1-5225-8437-7.ch006(119-143)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8437-7.ch006
Galke LMai FVagliano IScherp AMitrovic TZhang JChen LChin D(2018)Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject LabelsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209236(197-205)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209236
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Index Terms

An architecture for the aggregation and analysis of scholarly usage data
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Digital libraries and archives

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cover image ACM Conferences

JCDL '06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries

June 2006

402 pages

ISBN:1595933549

DOI:10.1145/1141753

General Chair:
Gary Marchionini
University of North Carolina, Chapel Hill
,
Program Chairs:
Michael L. Nelson
Old Dominion University
,
Catherine C. Marshall
Microsoft

Copyright © 2006 ACM.

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Published: 11 June 2006

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JCDL06

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JCDL06: Joint Conference on Digital Libraries 2006

June 11 - 15, 2006

NC, Chapel Hill, USA

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

Champiri ZFisher BKiong LDanaee M(2020)How Contextual Data Influences User Experience with Scholarly Recommender Systems: An Empirical FrameworkHCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies10.1007/978-3-030-60114-0_42(635-661)Online publication date: 3-Oct-2020
https://doi.org/10.1007/978-3-030-60114-0_42
Maake BOjo SZuva T(2019)A Survey on Data Mining Techniques in Research Paper Recommender SystemsResearch Data Access and Management in Modern Libraries10.4018/978-1-5225-8437-7.ch006(119-143)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-8437-7.ch006
Galke LMai FVagliano IScherp AMitrovic TZhang JChen LChin D(2018)Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject LabelsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209236(197-205)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209236
Wang JChen YPeng JChen IMcDonald RWorby NJatowt AMarshall CMilligan I(2017)Heterogeneous resources aggregation for literature usage analysis in academic librariesProceedings of the 17th ACM/IEEE Joint Conference on Digital Libraries10.5555/3200334.3200383(301-302)Online publication date: 19-Jun-2017
https://dl.acm.org/doi/10.5555/3200334.3200383
Wang JChen YPeng JChen I(2017)Heterogeneous Resources Aggregation for Literature Usage Analysis in Academic Libraries2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)10.1109/JCDL.2017.7991600(1-2)Online publication date: Jun-2017
https://doi.org/10.1109/JCDL.2017.7991600
Fan YGuo JLan YXu JCheng X(2017)Academic Access Data Analysis for Literature RecommendationInformation Retrieval10.1007/978-3-319-68699-8_4(42-54)Online publication date: 21-Oct-2017
https://doi.org/10.1007/978-3-319-68699-8_4
Beel JGipp BLanger SBreitinger C(2016)Research-paper recommender systemsInternational Journal on Digital Libraries10.1007/s00799-015-0156-017:4(305-338)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s00799-015-0156-0
Vellino A(2015)Recommending research articles using citation dataLibrary Hi Tech10.1108/LHT-06-2015-006333:4(597-609)Online publication date: 16-Nov-2015
https://doi.org/10.1108/LHT-06-2015-0063
Bento Fda Silva L(2013)Going Beyond the Bibliographic CatalogLibrary Automation and OPAC 2.010.4018/978-1-4666-1912-8.ch001(1-38)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-1912-8.ch001
Beel JLanger SGenzmehr MGipp BBreitinger CNürnberger ABellogín ACastells PSaid ATikk D(2013)Research paper recommender system evaluationProceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation10.1145/2532508.2532512(15-22)Online publication date: 12-Oct-2013
https://dl.acm.org/doi/10.1145/2532508.2532512
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