School SelectRApril 16, 2015
To get more experience with recommendation engines, I decided to try and create one using publicly available data from the National Center of Education statistics on college institutional characteristics. The end product is a content-based recommendation engine designed to take user input and output a table listing 4-year private and public colleges that best match this input. The recommendations are based on the Gower dissimilarity index, which can take variables of multiple types and also allows unique variable weights. The final result is an index on a scale from 0 to 1, where 1 represents a perfect match.
Data were taken from the publicly available IPEDS, from the National Center for Education Statistics. Only a small subset of variables were used in the matching process for simplicity, and are the following:
- geographic region
- sector (i.e., public or private)
- total price for out-of-state students living off campus (not with family)
- percent admitted - total
- Graduation rate total cohort after 6 years
This application was built using R and Shiny, and can be seen here. The defaults were set according to my preferences back in the day when I was thinking about what colleges to apply to. All the schools I applied to are actually shown in the top 10 with my alma mater coming in at number 8, which is a nice little validation check with respect to the matching algorithm. As always, the relevant code and data for this application can be seen on GitHub.