Hybrid Recommender for Research Papers and Articles

Alhassan Jamilu Ibrahim; Peter Zira; Nuraini Abdulganiyyi

doi:doi:10.11648/j.ijiis.20211002.11

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Hybrid Recommender for Research Papers and Articles

Alhassan Jamilu Ibrahim, Peter Zira, Nuraini Abdulganiyyi

Published in International Journal of Intelligent Information Systems (Volume 10, Issue 2)

Received: 18 September 2019 Accepted: 22 March 2021 Published: 14 May 2021

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Abstract

In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.

Published in	International Journal of Intelligent Information Systems (Volume 10, Issue 2)
DOI	10.11648/j.ijiis.20211002.11
Page(s)	9-15
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), 2021. Published by Science Publishing Group

Keywords

Recommender System, Content-Based Approach, Collaborative Filtering Technique, Hybrid Recommender, Digital Library

References

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APA Style

Alhassan Jamilu Ibrahim, Peter Zira, Nuraini Abdulganiyyi. (2021). Hybrid Recommender for Research Papers and Articles. International Journal of Intelligent Information Systems, 10(2), 9-15. https://doi.org/10.11648/j.ijiis.20211002.11

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ACS Style

Alhassan Jamilu Ibrahim; Peter Zira; Nuraini Abdulganiyyi. Hybrid Recommender for Research Papers and Articles. Int. J. Intell. Inf. Syst. 2021, 10(2), 9-15. doi: 10.11648/j.ijiis.20211002.11

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AMA Style

Alhassan Jamilu Ibrahim, Peter Zira, Nuraini Abdulganiyyi. Hybrid Recommender for Research Papers and Articles. Int J Intell Inf Syst. 2021;10(2):9-15. doi: 10.11648/j.ijiis.20211002.11

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@article{10.11648/j.ijiis.20211002.11,
  author = {Alhassan Jamilu Ibrahim and Peter Zira and Nuraini Abdulganiyyi},
  title = {Hybrid Recommender for Research Papers and Articles},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {2},
  pages = {9-15},
  doi = {10.11648/j.ijiis.20211002.11},
  url = {https://doi.org/10.11648/j.ijiis.20211002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211002.11},
  abstract = {In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.},
 year = {2021}
}

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TY  - JOUR
T1  - Hybrid Recommender for Research Papers and Articles
AU  - Alhassan Jamilu Ibrahim
AU  - Peter Zira
AU  - Nuraini Abdulganiyyi
Y1  - 2021/05/14
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211002.11
DO  - 10.11648/j.ijiis.20211002.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 9
EP  - 15
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211002.11
AB  - In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.
VL  - 10
IS  - 2
ER  -

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Author Information

Alhassan Jamilu Ibrahim

Faculty of Science, Federal University Kashere, Gombe, Nigeria
Peter Zira

Faculty of Science, Federal University Kashere, Gombe, Nigeria
Nuraini Abdulganiyyi

Faculty of Science, Federal University Kashere, Gombe, Nigeria

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Plain Text BibTeX RIS

APA Style

Alhassan Jamilu Ibrahim, Peter Zira, Nuraini Abdulganiyyi. (2021). Hybrid Recommender for Research Papers and Articles. International Journal of Intelligent Information Systems, 10(2), 9-15. https://doi.org/10.11648/j.ijiis.20211002.11

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ACS Style

Alhassan Jamilu Ibrahim; Peter Zira; Nuraini Abdulganiyyi. Hybrid Recommender for Research Papers and Articles. Int. J. Intell. Inf. Syst. 2021, 10(2), 9-15. doi: 10.11648/j.ijiis.20211002.11

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AMA Style

Alhassan Jamilu Ibrahim, Peter Zira, Nuraini Abdulganiyyi. Hybrid Recommender for Research Papers and Articles. Int J Intell Inf Syst. 2021;10(2):9-15. doi: 10.11648/j.ijiis.20211002.11

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@article{10.11648/j.ijiis.20211002.11,
  author = {Alhassan Jamilu Ibrahim and Peter Zira and Nuraini Abdulganiyyi},
  title = {Hybrid Recommender for Research Papers and Articles},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {2},
  pages = {9-15},
  doi = {10.11648/j.ijiis.20211002.11},
  url = {https://doi.org/10.11648/j.ijiis.20211002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211002.11},
  abstract = {In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Hybrid Recommender for Research Papers and Articles
AU  - Alhassan Jamilu Ibrahim
AU  - Peter Zira
AU  - Nuraini Abdulganiyyi
Y1  - 2021/05/14
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211002.11
DO  - 10.11648/j.ijiis.20211002.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 9
EP  - 15
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211002.11
AB  - In digital libraries and other e-commerce sites, recommender system is the solution that supports the users in information search and decision making. Some of these recommender systems will make predictions by matching the content of an item against the user profile otherwise known as Content-Based recommendation approach. Other recommenders will provide recommendation based on ratings of items from current user and other users and then use it to recommend similar items the current user has not seen, this is known as Collaborative-Based recommender approach. There exist several other approaches that are used in recommending articles and other items to users of different search engines. Over the years several researchers have tried combining these approaches in an attempt to design more efficient recommendations in search engines. This research proposed and designed a prototype of a Hybrid recommender called Zira, which is a model that combines both the Collaborative filtering, Content-based filtering, attribute-based approach to look at contextual information as well as an item-based approach that will solve the issues associated with cold-start problems all working concurrently to complement one another. The proposed system supports multi-criteria ratings, provide more flexible and less intrusive types of recommendations to ensure the improvement in recommendations of e-learning materials to users of digital libraries.
VL  - 10
IS  - 2
ER  -

Copy | Download