BibTeX:

@article{IJIRSTV2I11181,
     title={University Recommendation Engine for MS},
     author={Vidya Kapase, Nikita Shinde, Pooja Musale and Kanchan Paryani},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={11},
     pages={468--470},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I11181.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

People make decisions every day. There are too many choices and a little time to explore them all. Recommendation systems help people make decisions in these complex information spaces. Recommendation systems are a type of information filtering that presents lists of items (films, songs, books, videos, images, products, web pages) which are likely of user interest. Amazon, Last.fm, Ulike, iLike, Netflix, Pandora are the most popular recommender systems all over the world. Simply they compare user interest acquired from his/her profile with some reference characteristics and predict the rating that the user would give. Recommendation System is a subclass of information filtering system which takes input from users and provides User with the most suitable output to fulfill his requirements. This system is used to present a new college admission system using data mining techniques for tackling college admission prediction problems. This System uses content-based filtering to provide aspiring students (Master of Science) with the most appropriate choices of colleges based on different parameters. The system analyses the student academics, merit, background, student records and the college admission criteria. Then it predicts the likelihood of colleges the student may enter. In addition to the high prediction accuracy is an advantage, as the system can predict suitable colleges that match the students’ profiles and the suitable track channels through which the students are advised to enter.

MS, GRE, TOEFL, SOP