Personalized College Recommender: A System for Graduate Students Based on Different Input Parameters Using Hybrid Model


Authors : Ashutosh C. Harkare; Nishita Pali; Nikita Khivasara; Ishita Jain; Ravi Murumkar

Volume/Issue : Volume 6 - 2021, Issue 5 - May

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/2Tkrupj

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.

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