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Design and Implementation of Collaborative Filtering Application System using Apache Mahout -Focusing on Movie Recommendation System-

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dc.contributor.author이준호-
dc.contributor.author주경수-
dc.date.accessioned2021-08-11T16:23:44Z-
dc.date.available2021-08-11T16:23:44Z-
dc.date.created2021-06-17-
dc.date.issued2017-
dc.identifier.issn1598-849X-
dc.identifier.urihttps://scholarworks.bwise.kr/sch/handle/2021.sw.sch/8178-
dc.description.abstractIt is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.-
dc.language한국어-
dc.language.isoko-
dc.publisher한국컴퓨터정보학회-
dc.titleDesign and Implementation of Collaborative Filtering Application System using Apache Mahout -Focusing on Movie Recommendation System--
dc.title.alternativeDesign and Implementation of Collaborative Filtering Application System using Apache Mahout -Focusing on Movie Recommendation System--
dc.typeArticle-
dc.contributor.affiliatedAuthor주경수-
dc.identifier.doi10.9708/jksci.2017.22.07.125-
dc.identifier.bibliographicCitation한국컴퓨터정보학회논문지, v.22, no.7, pp.125 - 131-
dc.relation.isPartOf한국컴퓨터정보학회논문지-
dc.citation.title한국컴퓨터정보학회논문지-
dc.citation.volume22-
dc.citation.number7-
dc.citation.startPage125-
dc.citation.endPage131-
dc.type.rimsART-
dc.identifier.kciidART002247119-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorApache Mahout-
dc.subject.keywordAuthorUser-based Collaboration Filtering-
dc.subject.keywordAuthorMovie Recommender System-
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