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            <name>Title</name>
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    <name>PhD</name>
    <description>PhD Thesis</description>
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              <text>No Thesis</text>
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          <name>Title</name>
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              <text>An efficient framework for scientific article recommendation system  </text>
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          <name>Subject</name>
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              <text>Computer Science</text>
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              <text>Excess data makes it challenging to extract information that is relevant to a domain of study or research. Existing state-of-the-art systems focus
majorly on the selection of highly connected, prestigious and cited articles, regardless of the relevance of papers. To improve quality of findings,
recommender systems which are a subclass of information filtration systems are used. They filter out relevant information over prestigious data
from an existing repository of information. There are various sub-domains under recommender systems. This study focuses on citation
recommendation. Citations are an integral part of any scientific paper, academic dissertation or projects. Finding appropriate citations for any
work is a scholar's most time-consuming task. Thus, a well-defined citation recommendation system provides fulfillment and completeness for citing
the giants works. The thesis aims to study existing frameworks for citation recommendation systems and identify the best dataset to work on graph-
based recommender systems. A framework that recommends the most similar and relevant article to the user rather than prestigious authors or
papers is here by proposed. The study explores various machine learning and deep learning techniques and methods which can be used effectively
in recommending loosely connected yet highly relevant articles. </text>
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          <name>Creator</name>
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              <text>Nair, Akhil M.</text>
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              <text>Author's Submission</text>
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              <text>Christ(Deemed to be University)</text>
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          <name>Date</name>
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              <text>2022-01-01</text>
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              <text>George, Jossy P.</text>
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          <name>Rights</name>
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              <text>Open Access</text>
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          <name>Format</name>
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              <text>PDF</text>
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          <name>Language</name>
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              <text>English</text>
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          <name>Type</name>
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              <text>PhD</text>
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          <name>Identifier</name>
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              <text>&lt;a href="http://hdl.handle.net/10603/426631" target="_blank" rel="noreferrer noopener"&gt;http://hdl.handle.net/10603/426631&lt;/a&gt;</text>
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