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            <name>Title</name>
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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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          <name>Title</name>
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              <text>Content Based Scientific Article Recommendation System Using Deep Learning Technique</text>
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          <description>The topic of the resource</description>
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              <text>Apriori algorithm; Content-Based recommendation; Gated recurrent unit</text>
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              <text>The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers.  2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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          <name>Creator</name>
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              <text>Nair A.M.; Benny O.; George J.</text>
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          <name>Source</name>
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              <text>Lecture Notes in Networks and Systems, Vol-204 LNNS, pp. 965-977.</text>
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          <name>Publisher</name>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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          <name>Date</name>
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              <text>2021-01-01</text>
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          <name>Identifier</name>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-16-1395-1_70" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-16-1395-1_70&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111347166&amp;amp;doi=10.1007%2F978-981-16-1395-1_70&amp;amp;partnerID=40&amp;amp;md5=ecf5ffeac6b6829eea55c7d4b16afb02" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111347166&amp;amp;doi=10.1007%2f978-981-16-1395-1_70&amp;amp;partnerID=40&amp;amp;md5=ecf5ffeac6b6829eea55c7d4b16afb02&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 23673370; ISBN: 978-981161394-4</text>
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          <name>Format</name>
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              <text>Online</text>
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              <text>English</text>
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              <text>Conference paper</text>
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              <text>Nair A.M., Department of Computer Science, CHRIST (Deemed to Be University), Lavasa, Pune, India; Benny O., Department of Computer Science, CHRIST (Deemed to Be University), Lavasa, Pune, India; George J., Department of Computer Science, CHRIST (Deemed to Be University), Lavasa, Pune, India</text>
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