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A survey of book recommender systems.

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Title: A survey of book recommender systems.
Authors: Alharthi, Haifa1 halha060@uottawa.ca, Inkpen, Diana1 Diana.Inkpen@uottawa.ca, Szpakowicz, Stan1 szpak@eecs.uottawa.ca
Source: Journal of Intelligent Information Systems. Aug2018, Vol. 51 Issue 1, p139-160. 22p.
Subjects: Book selection, Reading, Mathematical domains, Set theory, Internet users
Abstract: The act of reading has benefits for individuals and societies, yet studies show that reading declines, especially among the young. Recommender systems can help stop such decline. We present a survey of recommender systems in the domain of books. We have categorized the systems into six classes, and highlighted the main trends, issues, evaluation approaches and datasets. Other research areas, such as psychology, are consulted to understand users’ books choices and reading models. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Information Systems is the property of Springer Nature 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: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A survey of book recommender systems.
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  Data: <searchLink fieldCode="AR" term="%22Alharthi%2C+Haifa%22">Alharthi, Haifa</searchLink><relatesTo>1</relatesTo><i> halha060@uottawa.ca</i><br /><searchLink fieldCode="AR" term="%22Inkpen%2C+Diana%22">Inkpen, Diana</searchLink><relatesTo>1</relatesTo><i> Diana.Inkpen@uottawa.ca</i><br /><searchLink fieldCode="AR" term="%22Szpakowicz%2C+Stan%22">Szpakowicz, Stan</searchLink><relatesTo>1</relatesTo><i> szpak@eecs.uottawa.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Information+Systems%22">Journal of Intelligent Information Systems</searchLink>. Aug2018, Vol. 51 Issue 1, p139-160. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Book+selection%22">Book selection</searchLink><br /><searchLink fieldCode="DE" term="%22Reading%22">Reading</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+domains%22">Mathematical domains</searchLink><br /><searchLink fieldCode="DE" term="%22Set+theory%22">Set theory</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+users%22">Internet users</searchLink>
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  Label: Abstract
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  Data: The act of reading has benefits for individuals and societies, yet studies show that reading declines, especially among the young. Recommender systems can help stop such decline. We present a survey of recommender systems in the domain of books. We have categorized the systems into six classes, and highlighted the main trends, issues, evaluation approaches and datasets. Other research areas, such as psychology, are consulted to understand users’ books choices and reading models. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Intelligent Information Systems is the property of Springer Nature 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: 10.1007/s10844-017-0489-9
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        Text: English
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      – SubjectFull: Book selection
        Type: general
      – SubjectFull: Reading
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      – SubjectFull: Mathematical domains
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      – SubjectFull: Set theory
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              Text: Aug2018
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