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 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">MOSCOW ECONOMIC JOURNAL</journal-id>
   <journal-title-group>
    <journal-title xml:lang="en">MOSCOW ECONOMIC JOURNAL</journal-title>
    <trans-title-group xml:lang="ru">
     <trans-title>Московский экономический журнал</trans-title>
    </trans-title-group>
   </journal-title-group>
   <issn publication-format="online">2413-046X</issn>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="publisher-id">74413</article-id>
   <article-categories>
    <subj-group subj-group-type="toc-heading" xml:lang="ru">
     <subject>Отраслевая и региональная экономика</subject>
    </subj-group>
    <subj-group subj-group-type="toc-heading" xml:lang="en">
     <subject></subject>
    </subj-group>
    <subj-group>
     <subject>Отраслевая и региональная экономика</subject>
    </subj-group>
   </article-categories>
   <title-group>
    <article-title xml:lang="en">ОПТИМИЗАЦИЯ ПОСАДОЧНЫХ СТРАНИЦ НА ОСНОВЕ КЛАСТЕРИЗАЦИИ ЗАПРОСОВ</article-title>
    <trans-title-group xml:lang="ru">
     <trans-title>ОПТИМИЗАЦИЯ ПОСАДОЧНЫХ СТРАНИЦ НА ОСНОВЕ КЛАСТЕРИЗАЦИИ ЗАПРОСОВ</trans-title>
    </trans-title-group>
   </title-group>
   <contrib-group content-type="authors">
    <contrib contrib-type="author">
     <name-alternatives>
      <name xml:lang="ru">
       <surname>Плотников</surname>
       <given-names>Андрей Викторович</given-names>
      </name>
      <name xml:lang="en">
       <surname>Plotnikov</surname>
       <given-names>Andrey Viktorovich</given-names>
      </name>
     </name-alternatives>
     <email>plotnikov-av@mail.ru</email>
     <xref ref-type="aff" rid="aff-1"/>
    </contrib>
   </contrib-group>
   <aff-alternatives id="aff-1">
    <aff>
     <institution xml:lang="ru">Пермский государственный аграрно-технологический университет имени академика Д.Н. Прянишникова</institution>
     <country>ru</country>
    </aff>
    <aff>
     <institution xml:lang="en">Пермский государственный аграрно-технологический университет имени академика Д.Н. Прянишникова</institution>
     <country>ru</country>
    </aff>
   </aff-alternatives>
   <pub-date publication-format="print" date-type="pub" iso-8601-date="2017-10-25T14:01:20+03:00">
    <day>25</day>
    <month>10</month>
    <year>2017</year>
   </pub-date>
   <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2017-10-25T14:01:20+03:00">
    <day>25</day>
    <month>10</month>
    <year>2017</year>
   </pub-date>
   <volume>2</volume>
   <issue>4</issue>
   <fpage>37</fpage>
   <lpage>37</lpage>
   <history>
    <date date-type="received" iso-8601-date="2017-10-07T14:01:20+03:00">
     <day>07</day>
     <month>10</month>
     <year>2017</year>
    </date>
    <date date-type="accepted" iso-8601-date="2017-10-15T14:01:20+03:00">
     <day>15</day>
     <month>10</month>
     <year>2017</year>
    </date>
   </history>
   <self-uri xlink:href="https://e-integral.ru/en/nauka/article/74413/view">https://e-integral.ru/en/nauka/article/74413/view</self-uri>
   <abstract xml:lang="ru">
    <p>В работе рассмотрено понятие кластеризации запросов как объединение схожих по интенту (смыслу, намерениям занятого поиском) запросов, независимо от их семантической релевантности. Представлены методы группировки запросов или soft- / hard-кластеризации. Визуально представлены hard-кластеризации с порогом 2 и 5. Показана на основе кластеризации с порогом 2 определено 93 группы, из них 58 групп, содержащих два и более запроса и 35 групп, содержащих 1 запрос. Методом hard-кластеризации с порогом 5 сформировано 167 групп, из них 96 групп, содержащих два и более запроса; 71 группа, содержащих 1 запрос.</p>
   </abstract>
   <trans-abstract xml:lang="en">
    <p>The paper considers the concept of query clustering is considered as an association of queries similar in meaning (meaning, intentions, occupied by the search), regardless of their semantic relevance. Methods for grouping queries or soft / hard-clustering are presented. Visually presented are hard-clustering with thresholds 2 and 5. Based on clustering with threshold 2, 93 groups are defined, of which 58 groups containing two or more requests and 35 groups containing 1 query are shown. Using the hard-clustering method with threshold 5, 167 groups were formed, of which 96 groups containing two or more requests; 71 group containing 1 query.</p>
   </trans-abstract>
   <kwd-group xml:lang="ru">
    <kwd>семантическое ядро</kwd>
    <kwd>кластеризация запросов</kwd>
    <kwd>классификация запросов</kwd>
   </kwd-group>
   <kwd-group xml:lang="en">
    <kwd>semantic core</kwd>
    <kwd>query clustering</kwd>
    <kwd>query classification</kwd>
   </kwd-group>
  </article-meta>
 </front>
 <body>
  <p>The paper considers the concept of query clustering is considered as an association of queries similar in meaning (meaning, intentions, occupied by the search), regardless of their semantic relevance. Methods for grouping queries or soft / hard-clustering are presented. Visually presented are hard-clustering with thresholds 2 and 5. Based on clustering with threshold 2, 93 groups are defined, of which 58 groups containing two or more requests and 35 groups containing 1 query are shown. Using the hard-clustering method with threshold 5, 167 groups were formed, of which 96 groups containing two or more requests; 71 group containing 1 query.</p>
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