Searching Similar Books Based on Student’s Preference for Personalized Education

Mingxi Zhang; Tianxing Liu; Xiaohong Wang; Liujie Sun

doi:doi:10.11648/j.sjedu.20170502.14

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Searching Similar Books Based on Student’s Preference for Personalized Education

Mingxi Zhang, Tianxing Liu, Xiaohong Wang, Liujie Sun

Published in Science Journal of Education (Volume 5, Issue 2)

Received: 1 March 2017 Accepted: 9 March 2017 Published: 25 March 2017

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Abstract

Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.

Published in	Science Journal of Education (Volume 5, Issue 2)
DOI	10.11648/j.sjedu.20170502.14
Page(s)	60-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Student’s Preference, Personalized Similarity Search, Personalized Education

References

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APA Style

Mingxi Zhang, Tianxing Liu, Xiaohong Wang, Liujie Sun. (2017). Searching Similar Books Based on Student’s Preference for Personalized Education. Science Journal of Education, 5(2), 60-65. https://doi.org/10.11648/j.sjedu.20170502.14

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ACS Style

Mingxi Zhang; Tianxing Liu; Xiaohong Wang; Liujie Sun. Searching Similar Books Based on Student’s Preference for Personalized Education. Sci. J. Educ. 2017, 5(2), 60-65. doi: 10.11648/j.sjedu.20170502.14

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AMA Style

Mingxi Zhang, Tianxing Liu, Xiaohong Wang, Liujie Sun. Searching Similar Books Based on Student’s Preference for Personalized Education. Sci J Educ. 2017;5(2):60-65. doi: 10.11648/j.sjedu.20170502.14

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@article{10.11648/j.sjedu.20170502.14,
  author = {Mingxi Zhang and Tianxing Liu and Xiaohong Wang and Liujie Sun},
  title = {Searching Similar Books Based on Student’s Preference for Personalized Education},
  journal = {Science Journal of Education},
  volume = {5},
  number = {2},
  pages = {60-65},
  doi = {10.11648/j.sjedu.20170502.14},
  url = {https://doi.org/10.11648/j.sjedu.20170502.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjedu.20170502.14},
  abstract = {Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.},
 year = {2017}
}

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TY  - JOUR
T1  - Searching Similar Books Based on Student’s Preference for Personalized Education
AU  - Mingxi Zhang
AU  - Tianxing Liu
AU  - Xiaohong Wang
AU  - Liujie Sun
Y1  - 2017/03/25
PY  - 2017
N1  - https://doi.org/10.11648/j.sjedu.20170502.14
DO  - 10.11648/j.sjedu.20170502.14
T2  - Science Journal of Education
JF  - Science Journal of Education
JO  - Science Journal of Education
SP  - 60
EP  - 65
PB  - Science Publishing Group
SN  - 2329-0897
UR  - https://doi.org/10.11648/j.sjedu.20170502.14
AB  - Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.
VL  - 5
IS  - 2
ER  -

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Author Information

Mingxi Zhang

College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, China
Tianxing Liu

College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, China
Xiaohong Wang

College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, China
Liujie Sun

College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, China

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Plain Text BibTeX RIS

APA Style

Mingxi Zhang, Tianxing Liu, Xiaohong Wang, Liujie Sun. (2017). Searching Similar Books Based on Student’s Preference for Personalized Education. Science Journal of Education, 5(2), 60-65. https://doi.org/10.11648/j.sjedu.20170502.14

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ACS Style

Mingxi Zhang; Tianxing Liu; Xiaohong Wang; Liujie Sun. Searching Similar Books Based on Student’s Preference for Personalized Education. Sci. J. Educ. 2017, 5(2), 60-65. doi: 10.11648/j.sjedu.20170502.14

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AMA Style

Mingxi Zhang, Tianxing Liu, Xiaohong Wang, Liujie Sun. Searching Similar Books Based on Student’s Preference for Personalized Education. Sci J Educ. 2017;5(2):60-65. doi: 10.11648/j.sjedu.20170502.14

Copy | Download

@article{10.11648/j.sjedu.20170502.14,
  author = {Mingxi Zhang and Tianxing Liu and Xiaohong Wang and Liujie Sun},
  title = {Searching Similar Books Based on Student’s Preference for Personalized Education},
  journal = {Science Journal of Education},
  volume = {5},
  number = {2},
  pages = {60-65},
  doi = {10.11648/j.sjedu.20170502.14},
  url = {https://doi.org/10.11648/j.sjedu.20170502.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjedu.20170502.14},
  abstract = {Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Searching Similar Books Based on Student’s Preference for Personalized Education
AU  - Mingxi Zhang
AU  - Tianxing Liu
AU  - Xiaohong Wang
AU  - Liujie Sun
Y1  - 2017/03/25
PY  - 2017
N1  - https://doi.org/10.11648/j.sjedu.20170502.14
DO  - 10.11648/j.sjedu.20170502.14
T2  - Science Journal of Education
JF  - Science Journal of Education
JO  - Science Journal of Education
SP  - 60
EP  - 65
PB  - Science Publishing Group
SN  - 2329-0897
UR  - https://doi.org/10.11648/j.sjedu.20170502.14
AB  - Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.
VL  - 5
IS  - 2
ER  -

Copy | Download