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Science-related Articles Recommendation System from Big Data

http://hdl.handle.net/20.500.12678/0000004195
37468aa1-082e-4833-8fe6-14ed34c50cfe
d6944ee9-244b-4a40-8c7d-97b1fd94f8cc
None
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20164.pdf 20164.pdf (111 Kb)
Publication type
Article
Upload type
Publication
Title
Title Science-related Articles Recommendation System from Big Data
Language en
Publication date 2016-02-25
Authors
Oo, Mie Khine
Khaing, Myo Kay
Description
Under the explosive increase of global data,the term Big data is mainly used to describeenormous datasets. With the availability ofincreasingly large quantities of digitalinformation, it is becoming more difficult forresearchers to extract and find relevant articlespertinent to their interests. In this system, wepropose an approach to discover andrecommend the desired articles by combiningcollaborative filtering (CF) with topic modeling.Correlated Topic Model (CTM) is used formodeling topics. Our approach not onlyconsiders the interactions between users throughcollaborative filtering but also learns theproperties of items involved through topicmodeling to improve recommendation. In orderto handle a large dataset, a Big data analyticstool Hadoop is used to perform processing overdistributed clusters. The proposed approachlearns the accuracy of the recommendation.
Keywords
Big data, Collaborative filtering, Topic modeling, Recommendation System
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/189
Journal articles
Fourteenth International Conference On Computer Applications (ICCA 2016)
Conference papers
Books/reports/chapters
Thesis/dissertations
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