College Recommendation System Using K-Means Algorithm

Abstract: Machine learning is one of the most exciting technologies of the 21st century. This is true both because of the complex issues it possesses the capability to solve, as well as its wide range of application areas. One of the most apparent applications of this technology is in the form of recommendation systems. The chapter outlines a clustering-based recommendation system and contrasts the viability of such systems in comparison with traditional recommender methodologies. The utility of such systems is further highlighted by the demonstration of an application. Utilizing massive online data and the foursquare application programming interface, the colleges across the US have been clustered into groups based on the similarity indexes. This segmentation is then used to present a rudimentary recommendation system that can prove to be useful for prospective students looking to make decisions about higher education institutions to find universities similar to the ones they already have in mind.

Keywords: Recommendation systems, clustering, K-means, college recommender

Recommended Citation: Chadha, H. , Gupta, S., Sharma, N., & Pathak, N. (2022). College Recommendation System Using K-Means Algorithm. In F. Ortiz-Rodriguez, S. Tiwari, S. Iyer, & J. Medina-Quintero (Eds.), Futuristic Trends for Sustainable Development and Sustainable Ecosystems (pp. 126-136). IGI Global. https://doi.org/10.4018/978-1-6684-4225-8.ch008

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Collaborators - Shruti Gupta, Dr. Neelam Sharma, Dr. Nitish Pathak