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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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              <text>A Pragmatic Study on Movie Recommender Systems Using Hybrid Collaborative Filtering</text>
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              <text>Cold-start; Collaborative filtering; Deep learning; First-rater; Gray sheep; KNN algorithm</text>
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              <text>The Movie Recommendation System (MRS) is part of a comprehensive class of recommendation systems, which categorizes information to predict user preferences. The sum of movies is increasing tremendously day by day, and a reliable recommender system should be developed to increase the user satisfaction. Most of the approaches are made to prevent cold-start, first-rater drawbacks, and gray sheep user problems, nevertheless, in order to recommend the related items, various methods are available in the literature. Firstly, content-based method has some drawbacks like data of similar user could not be achieved, and what category of these items the user likes or dislikes are also not known. Secondly, this paper discusses about collaborative filtering to find both user and item attributes that have been considered. Since there exist some issues pictured with collaborative filtering, so this paper further aims into hybrid collaborative filtering and deep learning with KNN algorithm of ratings of top K-nearest neighbors.  2022, 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>M. Nair A.; Preethi N.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-244, pp. 489-494.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-16-2919-8_44" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-16-2919-8_44&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115603007&amp;amp;doi=10.1007%2F978-981-16-2919-8_44&amp;amp;partnerID=40&amp;amp;md5=b579d922ef264b642d616ca23ac323cc" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115603007&amp;amp;doi=10.1007%2f978-981-16-2919-8_44&amp;amp;partnerID=40&amp;amp;md5=b579d922ef264b642d616ca23ac323cc&lt;/a&gt;</text>
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            <elementText elementTextId="182014">
              <text>ISSN: 23673370; ISBN: 978-981162918-1</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>M. Nair A., CHRIST (Deemed To Be University), Bengaluru, India; Preethi N., CHRIST (Deemed To Be University), Bengaluru, India</text>
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