Implementing a recommender system for Bangladeshi faculty search using machine learning
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Date
2019-12Publisher
Brac UniversityAuthor
Hasan, Md.KhalidMetadata
Show full item recordAbstract
Machine learning is a one of the popular fields in Computer Science. In my thesis
research the focus is to implement a recommender system for Bangladeshi faculty
search. Selecting a appropriate faculty or thesis supervisor is a very important part
in a student’s life. Even choosing right academy is also an important part in their
study life. This research paper presents a faculty recommender system to assist
students in making these choices. Here the main focus is to cover our own country,
Bangladesh, to help the students of our country to pursue their own interest. I
proposed this recommender system by using collaborative filtering algorithm. I
used a very popular machine learning algorithm, K-Nearest Neighbor algorithm
with cosine similarity to predict faculty members. It works on a vast database
and being analyzed by different criteria. It applies multiple filtering conditions to
retrieve relevant supervisor or faculty member based on the research interest or
preferences. The preference field of the faculties based on preferred research area,
making part of the decision specific. This system helps a user finding faculty or
supervisor according to own individual interests. It contains information about
faculties around Bangladesh from different institutions. A classification accuracy of
76.0 % for the predicted results ac hived by the proposed model.