Article Recommendations with Item-Based Collaborative Filtering on Online News Portals

Authors

  • Bram Bravo Universitas Budi Luhur, Indonesia
  • Indra Indra Universitas Budi Luhur, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v6i3.851

Keywords:

Item-based Collaborative filtering, Pageviews, ads, Traffic

Abstract

News portals generate additional traffic or traffic visits from the article recommendation widget. However, it is unfortunate that the traffic visits obtained from the widget are still relatively small. The article recommendation widget is rarely clicked by readers because the available recommended articles are less relevant to readers, resulting in one reader only reading no more than 2 articles obtained from the article recommendation widget. The purpose of this study is to further optimize the currently available article recommendation widget feature by adding reader interest data so that the number of articles read by one user will increase and will directly have an impact on increasing traffic visits. The method used in this study is Item Based Collaborative Filtering. After using the item-based filtering method by calculating the set of items x read and the duration of the reader's time in reading item x. In this study, a simulation was given to one of the reader samples and it was found that the highest interest of the reader sample was in reading sports news with a calculation score is 0.743210. The results of this study are article recommendations that match the reader's interests. The results of the study are expected to help users find articles that match their interests and preferences, so that they can increase the level of interaction and engagement with online media.

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Published

2024-09-24

Issue

Vol. 6 No. 3 (2024): September

Section

Articles

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  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

How to Cite

[1]
B. Bravo and I. Indra, “Article Recommendations with Item-Based Collaborative Filtering on Online News Portals”, journalisi, vol. 6, no. 3, pp. 1962–1972, Sep. 2024, doi: 10.51519/journalisi.v6i3.851.