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Developing a novel recommender network-based ranking mechanism for library book acquisition.

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Title: Developing a novel recommender network-based ranking mechanism for library book acquisition.
Authors: Wu, Fan1 misfw@mis.ccu.edu.tw, Hu, Ya-Han1 yahan.hu@mis.ccu.edu.tw, Wang, Ping-Rong1 misyhu@ccu.edu.tw
Source: Electronic Library. 2017, Vol. 35 Issue 1, p50-68. 19p.
Subject Terms: *Academic libraries, *Bibliometrics, *Collection development in libraries, *Decision making, *Data analysis, Literature, Statistics
Geographic Terms: Taiwan
Abstract: Purpose Most academic libraries provide book recommendation services to enable readers to recommend books to the libraries. To facilitate decision-making in book acquisition, this study aimed to develop a method to determine the ranking of the recommended books based on the recommender network.Design/methodology/approach The recommender network was conducted to establish relationships among book recommenders and their similar readers by using circulation records. Furthermore, social computing techniques were used to evaluate the degree of representativeness of the recommenders and subsequently applied as a criterion to rank the recommended books. Empirical studies were performed to demonstrate the effectiveness of the proposed ranking system. The Spearman’s correlation coefficients between the proposed ranking system and the ranking obtained using reader circulation statistics were used as performance measure.Findings The ranking calculated using the proposed ranking mechanism was highly and moderately correlated to the ranking obtained using reader circulation statistics. The ranking of recommended books by the librarians was moderately and poorly correlated to the ranking calculated using reader circulation statistics.Practical implications The book recommender can be used to improve the accuracy of book recommendations.Originality/value This study is the first that considers the recommender network on library book acquisition. The results also show that the proposed ranking mechanism can facilitate effective book-acquisition decisions in libraries. [ABSTRACT FROM AUTHOR]
Copyright of Electronic Library is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 121257328
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Developing a novel recommender network-based ranking mechanism for library book acquisition.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Fan%22">Wu, Fan</searchLink><relatesTo>1</relatesTo><i> misfw@mis.ccu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Ya-Han%22">Hu, Ya-Han</searchLink><relatesTo>1</relatesTo><i> yahan.hu@mis.ccu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Ping-Rong%22">Wang, Ping-Rong</searchLink><relatesTo>1</relatesTo><i> misyhu@ccu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Electronic+Library%22">Electronic Library</searchLink>. 2017, Vol. 35 Issue 1, p50-68. 19p.
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  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Academic+libraries%22">Academic libraries</searchLink><br />*<searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br />*<searchLink fieldCode="DE" term="%22Collection+development+in+libraries%22">Collection development in libraries</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Literature%22">Literature</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Taiwan%22">Taiwan</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose Most academic libraries provide book recommendation services to enable readers to recommend books to the libraries. To facilitate decision-making in book acquisition, this study aimed to develop a method to determine the ranking of the recommended books based on the recommender network.Design/methodology/approach The recommender network was conducted to establish relationships among book recommenders and their similar readers by using circulation records. Furthermore, social computing techniques were used to evaluate the degree of representativeness of the recommenders and subsequently applied as a criterion to rank the recommended books. Empirical studies were performed to demonstrate the effectiveness of the proposed ranking system. The Spearman’s correlation coefficients between the proposed ranking system and the ranking obtained using reader circulation statistics were used as performance measure.Findings The ranking calculated using the proposed ranking mechanism was highly and moderately correlated to the ranking obtained using reader circulation statistics. The ranking of recommended books by the librarians was moderately and poorly correlated to the ranking calculated using reader circulation statistics.Practical implications The book recommender can be used to improve the accuracy of book recommendations.Originality/value This study is the first that considers the recommender network on library book acquisition. The results also show that the proposed ranking mechanism can facilitate effective book-acquisition decisions in libraries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Electronic Library is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1108/EL-06-2015-0094
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        Text: English
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      – SubjectFull: Bibliometrics
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      – SubjectFull: Collection development in libraries
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      – SubjectFull: Decision making
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      – SubjectFull: Statistics
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      – SubjectFull: Taiwan
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      – TitleFull: Developing a novel recommender network-based ranking mechanism for library book acquisition.
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            NameFull: Wu, Fan
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            NameFull: Hu, Ya-Han
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              Text: 2017
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