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Science-related Articles Recommendation System from Big Data
http://hdl.handle.net/20.500.12678/0000004195
http://hdl.handle.net/20.500.12678/000000419537468aa1-082e-4833-8fe6-14ed34c50cfe
d6944ee9-244b-4a40-8c7d-97b1fd94f8cc
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Article | ||||||
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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 |