Graduate School Recommender System: Assisting Admission Seekers to Apply for Graduate Studies in Appropriate Graduate Schools Department of Computer Science and Engineering 1 University of Dhaka, 3 Primeasia University, 2,4 Bangladesh University of Engineering and Technology Mahamudul Hasan 1 , Shibbir Ahmed 2 , Deen Md. Abdullah 3 , and Md. Shamimur Rahman 4 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 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Accuracy Top-N Recommended Universities Accuracy 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Accuracy Percentage of training data (%) Accuracy Change due to Variation of 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)