In recent times, it is seen that many graduate
students are willing to learn in foreign universities.
Many factors drive students and experienced people to
apply for different colleges and universities such as
better opportunities of research, post-graduation, PhD
and wider exposure to grab work in plethora of jobs.
This situation is predominant in students from Indian
sub-continent and Asian countries. These students aim to
get admissions in many top universities of USA. As per
data, scores of exams like GRE, TOEFL, IELTS,
recommendation letters like SOPs and LORs, GPA of
UG play pivotal role in university admissions. We are
aiming to build a recommendation web platform which
will suggest users with top 3 recommended colleges
based on their profiles and inputs. As students spend a
lot on counseling for university recommendations, our
system holds a complete cost affordable platform for
accurate results and user preferences. Collaborative
filtering and content-based filtering is used to form a
hybrid model on various hidden attributes. In this paper
we summarized the methodology of underlined
algorithms and focused on different parameters which
will affect the overall recommendations.
Keywords : Model based Collaborative Filtering, Content based Filtering, Pearson’s Coefficient, Neural Network, Matrix Factorization, K-NN, Recommender Systems, TF_IDF Vectorization, Prediction, Preference.