A cold-start problem faced by a recommender system leads to serious causes and ruins the functionality of the entire system, sometimes responsible for losing new users also due to poor accuracy in recommendations. Recommendation becomes very rigid in case of a new recommender system where the product details exist but no user started viewing or rating the products yet. Similarly, when a new product is added the corresponding ratings are missing or when a new user enters the system, there is lack of knowledge about the preferences of the new user. This work concentrates on the aforementioned cold-start problems by designing a hybrid recommender engine for academic choices. Users’ preferences diverge time to time and domain to domain. Academia is one such field in which students’ feel more challenging to pick up their course after completing their school, which determines the future of a student. This may be due to either less perception about the available choices or more information overload in the internet. There is no single point of contact which helps the students to explore and suggest the enormous choices in education. Recommender system is a tool which suggests the users to find out the best products based on their tastes and needs. Another bigger challenge in this system is missing ratings. Existing user profiles represents the preferences alone and not the rating about the courses or institutes. This work proposes such a personalized recommender system which recommends opt courses for a student based on his expected score as well as preference. The proposed methodology was evaluated on real data set available from previous year engineering counselling conducted by Anna University. © BEIESP.