Abstract
This study aims to suggest a recommender System for undergraduate students who desire to seek admission into engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a recommender system for admission into the top 10 IIT on a pilot basis in four common branches such as Electrical Engineering, Computer Science and Engineering, Mechanical Engineering, Civil Engineering. Data were collected from different authentic sources from 2016 to 2018. A model was built to predict the ranks for 2019 for each branch of every IITs. This paper illustrates prediction using Time Series Forecasting and recommendation algorithm using classification techniques. A comparative study of Random Forest Classification and K-Nearest Neighbor classification has been done. Finally, the recommendation algorithm shown reliable results with high accuracy in prediction model. It can be diversify and implement other streams as part of future work.
Highlights
As RS has emerged as a demanding tool a short time ago, it is extremely important to have a proper understanding of it
One must focus on few important aspects of recommender systems such as "building a recommender system", different techniques to build or develop the recommender system such as DM, collaborative filtering, Content-Base filtering and Context-Aware methods
As the accuracy in K-Nearest Neighbor (KNN) is 94.11% which is better than the Random Forest Classification having accuracy 80%, so the proposed model is KNN
Summary
As RS has emerged as a demanding tool a short time ago, it is extremely important to have a proper understanding of it. One must focus on few important aspects of recommender systems such as "building a recommender system", different techniques to build or develop the recommender system such as DM, collaborative filtering, Content-Base filtering and Context-Aware methods. Different aspects that affect the RSD are the users, domains, and interfaces. One more important aspect is to safeguard the privacy of the user during this decision making process. The recommender system is used in a wide variety of fields such as music, movies, education, social networks, mobile computing, healthcare, insurance, e-commerce applications and many more. In the Internet era, the biggest problem for a person who wants to buy something online is how to get enough information to make a decision, and how to make a right decision with that enormous information
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