Machine Learning Business School Recommendation System
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Solution Overview
Problem
Current systems for recommending business schools lack data-driven and machine learning-based approaches, leading to subjective and biased recommendations, and do not provide real-time accurate matches between applicant profiles and school profiles.
Innovation Solution
A system and method that utilizes machine learning algorithms, specifically support vector machine regression (SVR) and K-nearest Neighbor algorithms, to create a predictive model for recommending business schools by mapping applicant profiles to school profiles, computing similarity scores, and providing a priority order of recommended schools based on admission data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional consultation services and guidebooks are used for business school selection, then applicants can obtain recommendations, but the recommendations are subject to bias and subjective evaluation
Solution Approach 1:
The patent replaces the mechanical/manual consultation system with an automated machine learning-based recommendation system. The system uses algorithms to process applicant profiles and school data, eliminating human subjectivity and bias while providing data-driven, accurate recommendations automatically.
Solution Approach 2:
The system enables applicants to independently input their own profile data and receive personalized school recommendations without requiring external consultants. The automated platform processes the information and generates tailored recommendations, allowing applicants to serve themselves.
2Productivity
If applicants manually shortlist business schools, then they can identify potential schools, but 20% of application cycle time is consumed by this process
Solution Approach 1:
The system performs preliminary matching of applicants with suitable business schools before the application process begins. By pre-processing applicant profiles and comparing them against school criteria, the system prepares a shortlist of recommended schools in advance, eliminating the need for time-consuming manual research during the application cycle.
Solution Approach 2:
The manual shortlisting process is replaced with an automated algorithmic system that rapidly processes applicant data and generates school recommendations instantaneously, dramatically reducing the time required from 20% of the application cycle to near-real-time.
3Reliability
If recommended schools are too aspirational for the applicant, then applicants may aim higher, but the match quality decreases and success rate drops
Solution Approach 1:
The system provides customized, localized recommendations tailored to each applicant's specific profile, goals, and circumstances. Rather than offering generic or uniformly aspirational lists, the algorithm adjusts recommendations to match the individual applicant's qualifications and aspirations, optimizing for both realism and ambition based on local context.
Data Source
AI summary
A system and method for recommending business schools based on an assessment of profiles of applicants and business schools. The method stores datasets pertaining to applicants, business schools, and admissions in a database through a storing module. Then the method reads and transforms the datasets into a structured dataset through a reading module. The method normalizes the datasets to provide a corresponding value to each dataset stored in the database through a data normalization module. The method maps first variables with second variables to compare the profile of the applicant and business school through a variable-mapping module. The method creates multi-label classification model through a multi-label classification module. The method computes a first similarity score between the profiles of the applicants and business schools through a similarity module and second similarity score by utilizing admission transactions data through a transaction-based matrix module.