Event management has evolved significantly with the rise of digital platforms, yet many existing systems lack personalisation, transparency, and efficient coordination. This paper presents EventVerse, an intelligent event planning and organization platform designed to simplify event management through automation and modern web technologies. The system enables users to discover, compare, and book event-related services, such as venues, photographers, and decorators, through a unified interface. Unlike traditional systems, EventVerse integrates user preferences, location data, and interaction history to provide personalized recommendations. The platform also ensures transparency through verified vendor profiles, real-time updates, and secure booking mechanisms powered by modern backend services. The proposed solution addresses limitations such as scattered information, unreliable reviews, and inefficient communication. Experimental implementation demonstrates improved user engagement, reduced planning time, and enhanced decisionmaking efficiency.
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Conclusion
This paper presented EventVerse, a smart web-based event planning and vendor recommendation system designed to address the limitations of traditional event management approaches. By leveraging modern web technologies and cloud-based backend services, the system provides a centralized, efficient, and userfriendly platform for event planning. The integration of personalization, real-time updates, and verified vendor information enhances both usability and reliability. Experimental results demonstrate the effectiveness of the proposed system in improving user engagement and reducing planning complexity. Future developments can further enhance the system’s capabilities, making it a robust solution for modern event management needs. This research presented the design and implementation of EventVerse, a smart web-based event planning and vendor recommendation system. The system addresses key challenges in traditional event planning, including lack of centralization, limited personalization, and inefficient communication. By leveraging modern web technologies and cloudbased infrastructure, EventVerse provides a scalable and efficient solution for managing event-related activities. The integration of recommendation mechanisms, realtime updates, and user-friendly interfaces enhances overall system performance. Experimental results and case study analysis demonstrate the effectiveness of the proposed approach in improving user experience and decision-making. The system has the potential to be extended with advanced features such as machine learning and predictive analytics, making it a valuable contribution to the field of event management systems.
References
1.P. Mateos and A. Bellogín, “A systematic literature review of recent advances on
context-aware recommender systems,” Artificial Intelligence Review, vol. 58, 2025.
2.
T. Kanwal and T. Amjad, “Research paper recommendation system based on multiple
features from citation network,” Scientometrics, vol. 129, pp. 5493–5531, 2024. 3.G. Park, L. Liss, and W. M. P. van der Aalst, “Learning recommendations from educational event data in higher education,” Journal of
Intelligent Information Systems, 2024. 4.V. B. Ingale and E. Saikiran, “Recommendation systems using event-based temporal data
model,” International Journal of Intelligent Systems and Applications in Engineering, 2023. 5.Y. An, Y. Tan, X. Sun, and G. Ferrari, “Recommender system: A comprehensive overview of technical challenges and social implications,” ICCK Transactions, 2024. 6.C. Bauer, A. Said, and E. Zangerle, “Evaluation perspectives of recommender systems: Driving research and education,” Dagstuhl Reports, 2024. 7.S.-Y. Lim, N. Hashim, and L. L. Thanh, “Recommender systems: A comprehensive review of models, approaches and evaluation metrics,” Journal of Informatics and Web
Engineering, vol. 4, no. 3, pp. 166–190, 2025. 8.Z. Xia et al., “Contemporary recommendation systems on big data and their applications: A survey,” IEEE Access, 2024. 9.Y. Li, K. Liu, R. Satapathy, S. Wang, and E.
Cambria, “Recent developments in
recommender systems: A survey,” 2023. 10.X. Ma, M. Li, and X. Liu, “Advancements in recommender systems: A comprehensive
analysis based on data, algorithms, and evaluation,” 2024.
How to Cite This Paper
Mrs Sheeba, Gunti Sneha, Karanam Thanmai, Arun Durai (2026). EventVerse: A Smart Web-Based Event Planning and Vendor Recommendation System Using Supabase and React. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.