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Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest
Author(s) -
Kavitha Muruganantham,
Subbaiah Shanmugasundaram,
A Dev,
A Mohan,
R Hu,
W Dou,
J Liu,
J Verma,
B Patel,
A Patel,
Z Sun,
B Wu,
Y Wu,
Y Ye,
R Wang,
H Cheng,
Y Jiang,
J Lou,
A Turk,
A Bilge,
R Zhang,
Q Liu,
J Wei,
G Xu,
Z Tang,
C Ma,
Y Liu,
M Daneshmand,
J Breese,
D Heckerman,
C Kadie,
F Zhang,
T Gong,
V Lee,
G Zhao,
C Rong,
G Qu,
M Zarei,
M Moosavi,
S Ghazarian,
M Nematbakhsh,
F Zhang,
Y Lu,
J Chen,
S Liu,
Z Ling,
W Serrano,
B Hammou,
A Lahcen,
S Mouline,
C Li,
K He,
B Hammou,
A Lahcen,
S Mouline,
M Hsieh,
T Weng,
K Li,
Y Zhang,
Y Zhou,
F Wang,
Z Sun,
Q He,
T Osadchiy,
I Poliakov,
P Olivier,
M Rowland,
E Foster,
A Costa,
M Manzato,
R Campello,
X Yuan,
L Han,
S Qian,
G Xu,
H Yan,
B Hammou,
A Lahcen,
S Mouline,
G Yatnalkar,
H Narman,
H Malik,
Q Shambour,
A Yassine,
L Mohamed,
M Achhab
Publication year - 2022
Publication title -
international journal of intelligent engineering and systems
Language(s) - English
Resource type - Journals
eISSN - 2185-310X
pISSN - 1882-708X
DOI - 10.22266/ijies2022.0831.40
Subject(s) - computer science , recommender system , random forest , factorization , matrix decomposition , matrix (chemical analysis) , artificial intelligence , machine learning , algorithm , eigenvalues and eigenvectors , composite material , quantum mechanics , materials science , physics

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