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Science-related Articles Recommendation System from Big Data

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dc.contributor.author Oo, Mi Khine
dc.contributor.author Khaing, Myo Kay
dc.date.accessioned 2022-04-24T08:08:32Z
dc.date.available 2022-04-24T08:08:32Z
dc.date.issued 2016-02
dc.identifier.uri https://onlineresource.ucsy.edu.mm/handle/123456789/2600
dc.description Science-related Articles Recommendation System from Big Data en_US
dc.description.abstract Under the explosive increase of global data, the term Big data is mainly used to describe enormous datasets. With the availability of increasingly large quantities of digital information, it is becoming more difficult for researchers to extract and find relevant articles pertinent to their interests. In this system, we propose an approach to discover and recommend the desired articles by combining collaborative filtering (CF) with topic modeling. Correlated Topic Model (CTM) is used for modeling topics. Our approach not only considers the interactions between users through collaborative filtering but also learns the properties of items involved through topic modeling to improve recommendation. In order to handle a large dataset, a Big data analytics tool Hadoop is used to perform processing over distributed clusters. The proposed approach learns the accuracy of the recommendation. en_US
dc.description.sponsorship University of Computer Studies, Yangon en_US
dc.language.iso en en_US
dc.publisher 14th International Conference on Computer Applications (ICCA 2016), Yangon, MYANMAR en_US
dc.relation.ispartofseries ;pp. 184-189
dc.relation.ispartofseries ICCA 2016;
dc.subject Big data en_US
dc.subject Collaborative filtering en_US
dc.subject Topic modeling en_US
dc.subject Recommendation System en_US
dc.title Science-related Articles Recommendation System from Big Data en_US
dc.type Article en_US

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