Authors : Atharva Amol Raut; Rushikesh M. Shete

Volume/Issue : Volume 10 - 2025, Issue 8 - August

Google Scholar : https://tinyurl.com/yj8pdtzh

Scribd : https://tinyurl.com/57354rwc

DOI : https://doi.org/10.38124/ijisrt/25aug1582

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : By using input from users about their academic qualifications or interests and preferred geographic location, EduSelect makes finding a college a custom process. With the increasing number of colleges and programs, students have many opportunities, but can often confuse choice with decision. EduSelect allowed students to navigate the ever-increasing number of choices when attending college. Moreover, EduSelect offers the user a research process that includes college profiles with detailed parameters and filters to information - application requirements, school information, rental housing costs, and scholarship opportunities. This report will discuss A) the platform's objectives; B) the technologies that were used to develop the application; C) the challenges that we faced; and D) the potential of EduSelect to guarantee that users are making an informed choice regarding their educational opportunities.

References :

1. Rajaraman, A. & Ullman, UL Haq, J. D. College of Engineering (2011). Mining of Massive Datasets. Cambridge University Press – Contains useful recommendation algorithms in for EduSelect.
2. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer, “Deep contextualized word representations” Proceedings of NAACL-HLT 2018.
3. Kumar, S., & Mehta, P. (2019). AI-Powered College Selection: A Review of Existing Approaches and Future Prospects. IEEE Access.
4. Garg, S., & Gupta, A. (2020). A Comparative Analysis of College Recommendation Systems Using Machine Learning. International Journal of Computer Applications, 176(39), 1-6. https://doi.org/10.5120/ijca2020919943
5. Niche (www.niche.com) – One of the most popular and comprehensive sources for personalized college recommendations based on user preferences.
6. MongoDB Documentation (www.mongodb.com/docs/). This is a reference for using a NoSQL database in EduSelect.
7. Jannach, D., & Adomavicius, G. (2016). Recommendation systems: Challenges, insights, and research opportunities. User Modeling and User-Adapted Interaction, 26(1), 1–2. https://doi.org/10.1007/s11257-016-9170-y
   This paper discusses modern challenges in recommender systems and approaches to user-centered platforms (e.g., EduSelect).
8. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey.Knowledge-Based.https://doi.org/10.1016/j.knosys.2013.03.012 This paper offers a broad comparison of collaborative, content-based, and hybrid recommender systems.
9. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S.( 2017). Neural Cooperative Filtering. Proceedings of the 26th International Conference on World Wide Web. https// doi.org/10.1145/3038912.3052569
   This paper discusses deep literacy- grounded recommendation models that may be applicable to bodying council recommendations.