<?xml version="1.0" encoding="UTF-8"?>
<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">journal-of-discrete-mathematical-sciences-and-cryptography</journal-id>
      <journal-title-group>
        <journal-title>Journal of Discrete Mathematical Sciences and Cryptography</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2169-0065</issn>
      <issn publication-format="print">0972-0529</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/JDMSC-1617</article-id>
      <title-group>
        <article-title>A landscape view of news recommendation systems based on MIND dataset</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Abdulhussein</surname>
            <given-names>Niran A.</given-names>
          </name>
          <aff>Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Obaid</surname>
            <given-names>Ahmed J.</given-names>
          </name>
          <aff>Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq</aff>
          <aff>National University of Science and Technology, Thi-Qar, Iraq</aff>
        </contrib>
      </contrib-group>
      <volume>26</volume>
      <issue>6</issue>
      <fpage>1705</fpage>
      <lpage>1716</lpage>
      <pub-date date-type="pub">
        <day>01</day>
        <month>06</month>
        <year>2023</year>
      </pub-date>
      <abstract>
        <p>Nowadays, it’s a very important way for researchers and all people to find their desired meaning instead of searching for a specific topic. Recommendation systems are strategies to solve the problems of search, finding or reducing the time of interest content for users under complex information environments. R.S. can show us the related results that are close to what we desire. In this paper, we list rates of the most used techniques applied over MIND dataset and show the results and comparisons among these techniques, we have proposed our analysis on that dataset, which has been collected from Microsoft in 2019, and we proposed new techniques and explain the views of researchers in previous studies and the techniques in R.S. that depend on.</p>
      </abstract>
      <kwd-group>
        <kwd>Recommendation system</kwd>
        <kwd>Click behavior</kwd>
        <kwd>MIND dataset</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
