Graduate School Recommender System: Assisting Admission Seekers to Apply for Admission Seekers in...
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Graduate School Recommender System: Assisting Admission Seekers to
Apply for Graduate Studies in Appropriate Graduate Schools
Department of Computer Science and Engineering 1University of Dhaka, 3Primeasia University, 2,4 Bangladesh University of Engineering and Technology
Mahamudul Hasan1, Shibbir Ahmed2, Deen Md. Abdullah3, and Md. Shamimur Rahman4
Introduction
To recommend accurately list of universities to apply for
graduate admission abroad considering applicant-applicant
similarity
Objective
To assist the graduate admission seekers in order to
apply for graduate admission to pursue higher study abroad
with funding
To develop a technique of using academic records of
successful applicants for making graduate school
recommender system
Methodologies
Flow Chart for Grad School
Recommender System
Design & Implementation
Experimental Evaluation & Analysis
Based on the experimental
analysis, if the number of
recommended universities
(N) is increased, the
accuracy is also increased.
Change of accuracy due to variation
of the training and test data
Top-N Recommended Universities vs. Accuracy
Conclusions
Our proposed recommender system will recommend list of
universities to applicants trying to pursue higher study abroad
Eventually assisting them to apply for graduate admission
in appropriate universities with best possible financial support
In the entire process of getting opportunity of graduate
studies university selection is the most crucial step for
applying to graduate admission
Knowledge discovery from the academic records of
successful graduate applicants is very important for the
graduate admission seekers in foreign institutions in respect
of choosing appropriate higher educational institute
5th International Conference on Informatics, Electronics & Vision (ICIEV)
13-14 May, 2016, Dhaka, Bangladesh
Compute a weighted
score from prior
information of successful
applicants such as
undergrad CGPA, GRE,
TOEFL Scores etc.
Calculate similarity
between weighted scores
using mean squared
deviation similarity metric
Test
Data Set
Recommend Top K
Universities to the test
users from N similar users
Training
Data Set
Compute a weighted
score from provided
information of current
applicants such as
undergrad CGPA, GRE,
TOEFL Scores etc.
Calculate top N similar users for
the Test users using K-nearest
neighbor algorithm
Recommend List of
Universities to users to apply
for graduate admission
Grad School
Recommender System
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Ac
cu
rac
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Top-N Recommended Universities
Accuracy
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
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Ac
cu
rac
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Percentage of training data (%)
Accuracy Change due to Variationof Training & Test Data
Based on the experimental
analysis, applicants get
admission into universities from
our recommended universities
in 65-70% cases of several training-testing dataset
Every time for each test
applicant’s computed weighted
score is considered with the
similar weighted score of those
applicants from training set, thus
we have recommended Top-N
universities (for our
experiment, N=10 is considered)