Design of an AI-Driven Smart Engineering Event Portal with Multi-Agent Recommendation and Analytics Framework

Authors

  • Prof. Jyotsna Nanajkar Guide, Department of Information Technology, Zeal College of Engineering and Research, Narhe, Pune, Maharashtra, India Author
  • Sanika Kulkarni Student, Department of Information Technology, Zeal College of Engineering and Research, Narhe, Pune, Maharashtra, India Author
  • Shravani Nandedkar Student, Department of Information Technology, Zeal College of Engineering and Research, Narhe, Pune, Maharashtra, India Author
  • Sujata Nikam Student, Department of Information Technology, Zeal College of Engineering and Research, Narhe, Pune, Maharashtra, India Author
  • Sejal Khopade Student, Department of Information Technology, Zeal College of Engineering and Research, Narhe, Pune, Maharashtra, India Author

DOI:

https://doi.org/10.32628/CSEIT251117145

Keywords:

Event Management System, Recommender System, Agentic AI, MERN Stack, Data Analytics, Multi-Agent Framework

Abstract

Academic event management continues to struggle with fragmentation, poor accessibility, and lack of personalization in today's digital education ecosystem. In order to improve event discovery and participation, this article outlines the architecture of an AI-driven Smart Engineering Event Portal that combines analytics, hybrid recommendation models, and multi-agent frameworks. The suggested system integrates content-based and collaborative filtering for personalized recommendations and Agentic AI for autonomous interaction, drawing on ideas from previous works such as AI-Based Event Management Web Application (Hada et al., 2022), Agentic AI Multi-Agent Recommender Framework (Portugal et al., 2024), and GoWIS Hybrid Event Recommendation System (Bhor et al., 2024). The solution, which was developed using the MERN stack, allows students to explore events according to their preferences while universities can use real-time dashboards to analyze participation.

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References

G.Liao, Y. Zhao, S. Xie, and P. S. Yu, Ineffective latent networks fusion based model for event recommendation in of ine ephemeral social net works, in Proc. 22nd Amin. Conf. Conf. Inf. Know. Manage. (CIKM), 2013, pp. 16551660. DOI: https://doi.org/10.1145/2505515.2505605

H. Ding, C. Yu, G. Li, and Y. Liu, Event participation recommendation in event-based social networks, in Social Informatics SocInfo (Lecture Notes in Computer Science), vol 10046, E. Spiro and Y. Y. Ahn, Eds. Cham, Switzerland: Springer, 2016. DOI: https://doi.org/10.1007/978-3-319-47880-7_22

F. Hao, S. Li, G. Min, H. C. Kim, S. S. Yau, and L. T. Yang, An ef cient approach to generating location-sensitive recommendations in ad-hoc social network environments, IEEE Trans. Services Comput., vol. 8, no. 3, pp. 520533, May 2015. DOI: https://doi.org/10.1109/TSC.2015.2401833

W. Zhang, J. Wang, and W. Feng, Combining latent factor model with location features for eventbased group recommendation, in Proc. 19th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2013, pp. 910918. DOI: https://doi.org/10.1145/2487575.2487646

T. J. Ogundele, C.-Y. Chow, and J.-D. Zhang, Eventrec: Personalized event recommendations for smart eventbased social networks, in Proc. IEEE Int. Conf. Smart Compute. (SMARTCOMP), May 2017, pp. 18. DOI: https://doi.org/10.1109/SMARTCOMP.2017.7947006

S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, ‘‘ReAct: Synergizing reasoning and acting in language models,’’ 2022, arXiv:2210.03629.

R. Yang, S. Lin, Y. Li, S. Zhao, Y. Ge, X. Li, and Y. Shan, ‘‘GPT4Tools: Teaching large language model to use tools via self-instruction,’’ in Proc. Adv. Neural Inf. Process. Syst., Jan. 2023, pp. 1–13.

S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, ‘‘Generative AI,’’ Bus. Inf. Syst. Eng., vol. 66, no. 1, pp. 111–126, Sep. 2023. DOI: https://doi.org/10.1007/s12599-023-00834-7

T. Chong, T. Yu, D. I. Keeling, and K. de Ruyter, ‘‘AI-chatbots on the services frontline addressing the challenges and opportunities of agency,’’ J. Retailing Consum. Services, vol. 63, Nov. 2021, Art. no. 102735. DOI: https://doi.org/10.1016/j.jretconser.2021.102735

L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, ‘‘Review of image classification algorithms based on convolutional neural networks,’’ Remote Sens., vol. 13, no. 22, p. 4712, Nov. 2021. DOI: https://doi.org/10.3390/rs13224712

M.S. Pera, , Ng, Y.-K.: Exploiting the wisdom of social connections to make personalized recommendations on scholarly articles. J. Intell. Inf. Syst. 42(3), 371–391 (2014). DOI: https://doi.org/10.1007/s10844-013-0298-8

R.L.,Alcamí,C.D.Carañana2012. Introduction to Management Information Systems. Primera edició

A. Sangràa , M.G. Sanmamed 2010. The role of information and communication technologies in improving teaching and learning processes in primary and secondary schools. ALT-J, Research in Learning Technology Vol. 18, No. 3 pp: 207-220. DOI: https://doi.org/10.1080/09687769.2010.529108

W.A Khattak , M .Nasir ,K. Sultan 2012. The Role of Information Technology in Media Industry. International Journal of Business and Social Science Vol. 3 No. 6 DOI: https://doi.org/10.29333/ojcmt/2395

A.-L Barabasi ´ (2002). Linked: The New Science of Networks. Cambridge, Massachussets: Perseus. [16] C. Jones,(2004). Networks and learning: communities, practices and the metaphor of networks. Research in Learning Technology, 12 (1). Retrievefrom. DOI: https://doi.org/10.3402/rlt.v12i1.11227

Website hosting using amazon web services for internet of things applications md. Abdul kadar, Department of computer science, Emirates college of Technology, Abudhabi

Jubin Dipakkumar Kothari (2018). A Case Study of Image Classification Based on Deep Learning Using Tensorflow International Journal of Innovative Research in Computer and Communication Engineering, Vol. 6, Issue 4, April 2018, 3888- 3892

Anitha Eemani,(2019). Network Optimization and Evolution to Bigdata Analytics Techniques, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 8, Issue 1, January 2019.

H. Lam, -y. (2007). A Learning Approach to SPAM Detection Based on Social Network. The Hong Kong University of Science and Technology, Hong Kong

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Published

05-11-2025

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Research Articles

How to Cite

[1]
Prof. Jyotsna Nanajkar, Sanika Kulkarni, Shravani Nandedkar, Sujata Nikam, and Sejal Khopade, “Design of an AI-Driven Smart Engineering Event Portal with Multi-Agent Recommendation and Analytics Framework”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 6, pp. 01–06, Nov. 2025, doi: 10.32628/CSEIT251117145.