TY  - JOUR
T1  - Cuckoo Search Based Personalized View for Movie Recommendation over Social Networks
AU - Uma Shankari, S. AU - Chidambaram, M. 
JO  - Research Journal of Applied Sciences
VL  - 15
IS  - 3
SP  - 102
EP  - 111
PY  - 2020
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2020.102.111
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2020.102.111
KW  - Recommender system
KW  -movie
KW  -features
KW  -clustering
KW  -context and probabilistic matrix factorization
AB  - Owing to the exponential growth of
information in online social networks, the users of such
networks demand the recommendation systems to deliver
significant results. A recommendation system rightly
suggests the personalized movies that are desirable to the
users predominantly from large information storage.
Notably, the current research works in movie
recommendation system focus on determining the most
relevant features from the user profile information and
shared contents in the social network. Even though the
existing research works recommend the movies that are in
proximity to the user preferences, there is a profound need
for further exploring the features of the movie and thus
ensure the highly desired movies to the users. Hence, this
paper targets on recommending the movies with the
knowledge of analyzing the movie features along with the
data clustering and computational intelligence methods.
This article proposes the Cuckoo search based MOst
personalized VIEw in item recommendation (CMOVIE)
model, incorporating the missing rating
prediction and contextual movie recommendation phases.
At first, the C-MOVIE approach explores the features of
the movies to recognize the interest of the users in terms
of inherent features after reducing the feature
dimensionality by Principal Component Analysis (PCA)
method. Then, it clusters the users based on the
recognized features by K-means clustering and Cuckoo
search optimization methods with the intention of
grouping the users with similar interests which eases the
missing rating prediction when using Probabilistic Matrix
Factorization (PMF). In the end, the C-MOVIE approach
contextually recommends the movies to the users by
mapping the features of the new movies with the features
of the clustered users. The experimental results yielded
on Douban movie which data set demonstrate that the CMOVIE
approach distinctively delivers the personalized
movie recommendation than the existing HPSO method.
ER  -