Ideaspire: Students Project Recommendation System
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International Journal of Recent Engineering Science (IJRES) |  |
| © 2024 by IJRES Journal | ||
| Volume-11 Issue-3 |
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| Year of Publication : 2024 | ||
| Authors : Kunal Bhangale, Shivani Bhoi, Pratham Mahajan, Yugandhara Bhagwat, Pradnya Vikhar |
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| DOI : 10.14445/23497157/IJRES-V11I3P116 |
How to Cite?
Kunal Bhangale, Shivani Bhoi, Pratham Mahajan, Yugandhara Bhagwat, Pradnya Vikhar, "Ideaspire: Students Project Recommendation System," International Journal of Recent Engineering Science, vol. 11, no. 3, pp. 134-137, 2024. Crossref, https://doi.org/10.14445/23497157/IJRES-V11I3P116
Abstract
In the critical last year of a four-year degree engineering program, choosing an appropriate project can represent the deciding moment of the graduation experience. Conventional strategies frequently leave understudies attempting to distinguish a theme that lines up with their inclinations, skills, and boss mastery. This study proposes a clever methodology using AI to upset the last undertaking point choice. By utilizing a cooperative separating calculation, the framework dissects understudy profiles - enveloping interests, abilities, and manager connections - close by an immense store of past venture information addressed by watchword records. This permits the framework to distinguish solid connections between understudy profiles and past tasks, at last suggesting points with a serious level of personalization. Past simple subject ideas, the examination digs into laying out a system that corresponds understudy scholarly qualities with project types. This structure determines the proposed framework and, in addition holds the possibility to develop the general last task insight further. The center point is to streamline the interaction by limiting sitting around and guaranteeing major areas of strength between understudy capacities and task requests. This approach can smooth out the last task venture, lessen understudy disappointment, and eventually add to a higher achievement rate for graduating understudies
Keywords
Education, Project selection, Natural Language Processing (NLP).
Reference
[1] Cut Fiarni, Herastia Maharani, and Billy Lukito, “Recommender System of Final Project Topic Using Rule-based and Machine Learning Techniques,” 2021 8th International Conference on Electrical Engineering, Computer Science and Informatics, Semarang, Indonesia, pp. 216-221, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Fatemeh Abbasi, and Ameneh Khadivar, “Collaborative Filtering Recommendation System through Sentiment Analysis,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 14, pp. 1843-1853, 2021.
[Google Scholar] [Publisher Link]
[3] Research Paper On Recommendation System, 2021 Dr. Alka Singhal, Shivangi Rastogi, Nikhil Panchal, Shivani Chauhan and Shradha Varshney.
[4] Shailesh D. Kalkar, and Pramila M. Chawan, “Recommendation System using Machine Learning Techniques,” International Research Journal of Engineering and Technology, vol. 9, no. 9, pp. 1038-1043, 2022.
[Google Scholar] [Publisher Link]
[5] Deepjyoti Roy, and Mala Dutta, “A Systematic Review and Research Perspective on Recommender System,” Journal of Big Data, vol. 9, pp. 1-36, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Kurt VanLehn et al., “Teaching Algebraic Model Construction: A Tutoring System, Lessons Learned and An Evaluation,” International Journal of Artificial Intelligence in Education, vol. 30, pp. 459-480, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Gemma Conroy, “How ChatGPT and Other AI Tools could Disrupt Scientific,” Nature, vol. 622, no. 7982, pp. 234-236, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Vladimiro Lovera Rulfi, and Ivan Spada, “Guidelines for the Use of Generative AI in Research Paper Writing,” GENERAL’23: GENerative, Explainable and Reasonable Artificial Learning Workshop, 2023.
[Google Scholar] [Publisher Link]
[9] Peter W. Foltz, GPT-3’s performance approached human-level on certain tasks” and formed the basis for ChatGPT (OpenAI, 2022) with some modifications. In spring 2023, ChatGPT was upgraded to utilize GPT- 4 (OpenAI, 2023).