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Hybrid Recommender for Research Papers and Articles

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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Cite This Article
  • 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
  • Faculty of Science, Federal University Kashere, Gombe, Nigeria

  • Faculty of Science, Federal University Kashere, Gombe, Nigeria

  • Faculty of Science, Federal University Kashere, Gombe, Nigeria

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