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Open access Jurnal SINTA 1

A User- Based Recommendation with a Scalable Machine Learning Tool

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 5 No. 5 (2015)

DOI
10.11591/ijece.v5i5.pp1153-1157
ISSN
2088-8708, 2088-8708
Published
2015-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.

Collaboration network of the authors

6 authors · 7 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Koneru Lakshmaiah Education Foundation
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Deepthi GurramKoneru Lakshmaiah Education Foundation1
Pellakuri VidyullathaKoneru Lakshmaiah Education Foundation1
Haritha AkkineniKoneru Lakshmaiah Education Foundation1
P.V.S LakshmiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>A User- Based Recommendation with a Scalable Machine Learning Tool</dc:title><dc:creator>Veena, Ch.</dc:creator><dc:creator>Babu, B. Vijaya</dc:creator><dc:description>&lt;jats:p&gt;Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2015-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v5i5.pp1153-1157</dc:identifier><dc:identifier>10.11591/ijece.v5i5.pp1153-1157</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 5 No 5 (2015); 1153</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/5737/4614</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>