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  <Article>
    <Journal>
      <PublisherName>ijesm</PublisherName>
      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 6, Issue 8,</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>December 2017 (Special Issue)</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2017</Year>
        <Month>12</Month>
        <Day>24</Day>
      </PubDate>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>Social Recommendation System Based on User Preference Learning</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>962</FirstPage>
      <LastPage>969</LastPage>
      <AuthorList>
        <Author>
          <FirstName>M.Sravani</FirstName>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
          <FirstName>Dr. A.Venkata</FirstName>
          <LastName>Ramana</LastName>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>Social recommendation system has become one of the most important applications in different research societies for information recovery which uses machine learning and data mining methods and ecommerce websites like Amazon and Durban. But the systems which are used by them have lot of constraints which are acute in the retrieval process. Since the constraints are crucial in the process, the proposed application should be in line with the all social requirements. We present a new and updated with all new recent developments in the social networks keeping in the mind the  graph online regularized user preference learning (GORPL), which integrates both collaborative user-item relationship as well as item content features into a unified preference learning process .In addition we develop OGRPL-FW which applies the Frank-Wolfe algorithm for efficient iterative procedure</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Userpreference; e-commerce; social recommendation; Social networks.</Keywords>
      <URLs>
        <Abstract>https://www.ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=4328&amp;title=Social Recommendation System Based on&#13;
User Preference Learning</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
  </Article>
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